How to stay ahead of the AI curve in 2025 (a developer's perspective)

Here's how to stay ahead of the curve as AI continues to progress at breakneck speed.

In recent months, we have witnessed a few trends in artificial intelligence that, if sustained, can help us better understand where the field is headed. Barring a huge surprise (which is not impossible given the current state of the field), we can leverage these trends to stay ahead of the curve and ship applications on time and budget.

Plummeting costs of inference

The one constant in the past few years has been the decreasing cost-per-token of LLMs. for example, the price per million tokens of OpenAI’s top-performing model has dropped by more than 200X in the past two years.

This trend will likely continue as hardware manufacturers build better accelerators and AI labs continue to compete for market share.

Why does it matter? Start experimenting with the most advanced LLMs and build application prototypes regardless of the costs. Soon enough, you will be able to deploy it at scale as the costs will continue to decrease.

Reasoning models

OpenAI o1 triggered a new wave of innovation around inference-time scaling, where the models use tokens to generate more answers, revise their responses, and explore different reasoning paths.

Why does it matter? Reasoning models are here to stay and will be a big theme in 2025. Make sure to experiment with them (including open-source alternatives to o1) and explore the frontier of what is possible. Test the limits and think about the kinds of applications that would be possible if these limitations were overcome (they probably will in the next generation). And don’t worry about costs—they will eventually drop.

Transformer alternatives

The memory and compute bottleneck of Transformers has given rise to alternative models such as state-space models (SSM), Liquid Neural Networks (LNNs), as well as hybrid models that combine Transformers with linear-compute models.

These models do not match the performance of cutting-edge Transformer-based models yet. But they are catching up fast.

Why does it matter? Experimenting with new model architectures will give you a feel of where the future of the field might be headed. If progress in the field continues, many simpler LLM applications can be offloaded to these models and run on edge devices or local servers, which can be a big deal for applications that must handle sensitive information.

What other trends are you watching? Let me know in the comments.