Infrastructure decisions have a long tail. The choices you make in 2026 about how to build, deploy, and operate AI systems will continue to affect your team's capabilities, costs, and flexibility for years. Some of these choices are good ones that will hold up. Others are likely to look like significant errors in retrospect.
Over-Optimizing for Current Model Prices
One of the most common patterns I see is teams making deep architectural commitments to save money on today's inference costs. Building custom inference pipelines, negotiating complex reserved capacity agreements, or investing in heavy infrastructure optimization to shave margins off current API pricing.
Model pricing has declined consistently for three years and shows no sign of stopping. A system that required heroic engineering to be profitable at $0.01 per thousand tokens will look very different at $0.001 per thousand tokens. The teams that will be in the best position are the ones that built flexible infrastructure that can take advantage of price drops, not the ones that optimized for today's economics at the cost of flexibility.
The exception is genuinely high-volume, latency-sensitive workloads where optimization produces compounding returns. But for most applications, the math on heavy infrastructure investment does not work out by the time you account for engineering opportunity cost.
Building on Single Provider APIs Without Abstraction
Vendor lock-in through hard-coded API calls is a choice that will age poorly. Teams that build deep integrations with a single provider's specific API shapes, response formats, and capabilities will find migration significantly more expensive than it needed to be when better options emerge or pricing changes.
This is not an argument against using frontier models. It is an argument for building abstraction layers that keep your application decoupled from your provider. The teams that built abstractions early have been able to seamlessly switch providers as pricing and capabilities have evolved. The teams that built direct integrations are paying for that convenience now.
Underinvesting in Evaluation Infrastructure
The teams that will be in the best position in two years are the ones building robust evaluation frameworks now, even when it feels premature. Evaluation is the foundation for every other AI infrastructure decision: model selection, prompt iteration, regression detection, performance monitoring. Without it, you are making all of those decisions on vibes rather than data.
The teams that have invested in evaluation infrastructure are shipping with confidence and catching regressions before they reach production. The teams that have not are discovering problems when customers do. This gap will compound over time.
The Overlooked Long Game
The AI infrastructure choices that will age best are not the ones that look clever today. They are the ones that preserve optionality, keep your team's decision-making capacity intact, and build toward capabilities rather than just current requirements. The teams that are thinking three years out are making different choices than the teams optimizing for this quarter's metrics. The difference will be visible by 2028.