AI model competition & fragmentation
The AI model market is fragmenting fast — a new model launches almost every week, and today's best model may not be next month's best model. For business leaders, the takeaway isn't to track every launch. It's to build your AI infrastructure so you can swap models without rebuilding everything. Companies that lock into a single model vendor today will face the same painful migration they went through moving off legacy software platforms.
Go deeper
Six months ago you chose an AI vendor built on a specific model. That model was best-in-class at the time. Today, a competitor's model outperforms it on the exact tasks you use it for, at half the cost. But switching means rewriting your prompts, re-testing your workflows, and retraining your team. You're stuck — not because the new option isn't better, but because you built on a foundation that assumed the landscape wouldn't shift.
The trap most companies fall into is treating their first AI vendor choice like a marriage. It's a lease. Build your AI workflows in layers: the business logic and data on your side, the model as a replaceable component on the other side. If your vendor's entire value proposition is 'we use Model X,' ask what happens when Model X is no longer the best option.
Questions to ask
- If we needed to switch AI models in 30 days, what would break and what would carry over?
- Does our AI vendor use a single model, or can they swap to better alternatives as the market shifts?
- Are our prompts, workflows, and training data documented in a way that's portable between providers?