The AI Loyalty Question: Why We’re All Beautiful Betrayers Now
And why that’s exactly how it should be in 2025
The confession every founder is thinking but not saying out loud:
I love our AI stack. But am I loyal? Will I stick with any of them if something materially better comes around?
Probably not.
It’s not like B2B from 2005-2022. You’d invest deeply in an app. It wouldn’t change all that much, but it was a market leader or becoming one. You’d get better and better at it. You’d stay for years. And if they treated you well? You were loyal.
Today? I’m grateful for our AI vendors. But I’m not quite … loyal. Is anyone anymore?
The Great AI Experiment We’re All Living Through
Walk into any startup today and you’ll see something wild: engineers openly jumping between Cursor, Windsurf, Loveable, Replit, and Bolt—sometimes within the same day. Marketing teams testing Claude against GPT-4 for different use cases. Product managers running parallel experiments with multiple AI coding assistants.
This isn’t tool ADD. This is survival.
We’re not trying these tools because they’re shiny (though they are). We’re trying them because the AI landscape is moving so fast that what seemed impossible last quarter is now table stakes. The tool that revolutionized your workflow in September might feel clunky by December.
Real talk: The improvement pace is unprecedented. When your current tool improves 2x in capability every six months, but a new tool launches that’s already 3x better, the math is simple.
Why Old-School Vendor Loyalty is Dead (For Now)
The incumbency advantage is weaker than ever. Yes, the players with the most training data and investment—your OpenAIs, your Anthropics—have a higher bar for disruption. But that bar isn’t insurmountable. We’ve watched startups come out of nowhere with specialized AI tools that instantly became must-haves.
The switching costs are there, but we’re up for it — for now. Unlike enterprise software of the past, most AI tools are designed for rapid adoption. No six-month implementations. No massive data migrations. Often, it’s as simple as changing an API endpoint or downloading a new extension. And we’re all in a mode where we are willing to invest more time (and money) in new vendors. Often for the first time in many years.
Everyone is a beginner again. Even the most experienced engineers are learning these tools from scratch. There’s no decades of muscle memory to overcome. This creates an unusual openness to experimentation that we haven’t seen since the early smartphone app ecosystem.
The Psychology of Why We’re All Tool Polygamists Now
The fear of missing out is rational. Unlike traditional FOMO, AI FOMO is justified. The competitive advantages from using better AI tools are immediate and measurable. Code quality, development speed, creative output—these aren’t marginal gains. They’re step-function improvements.
The community is driving everything. Watch Twitter or Discord communities around AI tools. Recommendations spread like wildfire. When a respected developer shares their new AI coding setup, hundreds of others try it within hours.
The pace of change is so fast — and exciting. Things are getting better even week. You want to bet on a winner, but you need to be open than a new entrant could be dramatically better. Just next week.
We’re all still figuring this out. The best practices for AI tool adoption are being written in real-time. By us. Right now.
As Aaron Levie Said: No One Can Take Their Position for Granted Today:
What This Actually Means for Your Business
Budget for AI churn. Plan for your AI stack to be 50-90% different by 2026. This isn’t a bug—it’s a feature. Budget accordingly, both financially and culturally.
Embrace portfolio thinking. Don’t just pick one AI coding assistant, one AI writing tool, one AI design tool. Build a portfolio. Test multiple options for each use case. The smartest teams we know are running parallel experiments constantly.
Train capabilities, not tool dependencies. The specific tool matters less than understanding how to effectively prompt, iterate, and integrate AI into workflows. Build muscle memory around AI thinking, not AI button-clicking.
Make switching decisions fast. In traditional enterprise software, switching decisions took quarters. In AI, you should be able to make the call in weeks. Set up regular “AI audits” where you evaluate new tools against your current stack.
The Vendor Reality Check
Every AI company faces the same challenge: how do you build sustainable competitive advantages when switching costs are low and the pace of innovation is exponential?
The smart ones aren’t fighting this reality—they’re embracing it. They’re building for a world where users expect to be able to try, adopt, and potentially switch tools rapidly.
The best AI tools know this. They’re not trying to lock you in—they’re trying to be so obviously better that you choose to stay. Every day.
Looking Ahead: The Stack That Doesn’t Exist Yet
Will your AI stack look completely different in 18 months? Almost certainly.
The companies winning in this environment aren’t the ones building moats—they’re the ones building experiences so good that customers don’t want to leave, even when they easily could.
Your current AI tools expect this. The mature ones are building for a world where customer loyalty is earned fresh every day, not locked in through contracts and integration complexity.
The Bottom Line
Here’s what we learned: Loving your current AI stack and being prepared to replace it aren’t contradictory positions. They’re complementary strategies for navigating the fastest-moving technology landscape in decades.
Use your current tools fully. Extract every bit of value. Be grateful for how they’re transforming your business. But keep your eyes open, your switching muscles flexible, and your willingness to experiment high.
The age of beautiful betrayal is here. The tools that serve you best today should expect—and respect—that you’ll move on when something better comes along.
Because the moment something delivers significantly more value, saves more time, or creates better outcomes? We’ll test it. Staying loyal to tools instead of staying loyal to results is how you get left behind.
Truth bomb: We’re still in the stage of AI where we should be trying one new tool a week.
And the truly great AI tools? They’re not just okay with this reality. They’re building for it.
