The Model You Love Is Probably Just the One You Use

Ask 10 developers which large language model (LLM) they’d recommend, and you’ll get 10 different answers—and almost none of them are based on objective comparison. What you’ll get instead is a reflection of the models they happen to have access to, the ones their employer approved, and the ones that influencers they follow have been quietly paid to promote. We’re all living inside recursively nested walled gardens, and most of us don’t realize it.

The access problem

In corporate environments, the model selection often happens by accident. Someone on the team tries Claude Code one weekend, gets excited, tells the group on Slack, and suddenly the whole organization is using it. Nobody evaluated alternatives. Nobody ran a bakeoff. The decision was made by whoever had a company card and a free Saturday. That’s not a criticism—it’s just how these things go. But it means that when that same person tells you their favorite model, they’re really telling you which model they’ve had the most reps with. There’s a genuine learning function at play: You get faster, your prompts get better, and the model starts to feel almost intuitive. It’s not that the model is objectively superior. It’s that you’ve gotten good at using it.

This matters more than people admit, because a lot of this space runs on feelings rather than evidence. People feel good about Opus right now. It feels powerful; it feels smart; it feels like you’re using the best tool available. And maybe you are. But ask someone who’s paying for their own tokens whether they feel the same way, and you tend to get a more calibrated answer. Skin in the game has a way of sharpening opinions.

The influence problem

There’s also a lot of money moving through this space in ways that don’t always get disclosed. Model providers are spending real budget to make sure the right people have the right experiences—early access, credits, invitations to exclusive communities. These perks can create a sense of exclusivity and superiority, making users more likely to champion the model they’ve been given.

Influencers play a significant role in this ecosystem. They often receive financial incentives to promote certain models, and their endorsements can sway public opinion. This creates a feedback loop where popular models become more popular, and less-known models struggle to gain traction.

The result is a fragmented landscape where the models we love are often the ones we use, rather than the ones that are objectively best. This isn’t necessarily a bad thing—different models have different strengths, and the right choice can depend on specific use cases. However, it’s important to be aware of the biases that influence our decisions.

Ultimately, the key to making informed choices lies in understanding the factors that shape our perceptions. By recognizing the role of access, influence, and personal experience, we can better evaluate the models we encounter and make more deliberate decisions about which ones to prioritize. In a world where the walled gardens are ever-expanding, it’s crucial to stay critical and open-minded.