The year ahead in AI is less about breakthrough moments and more about what we actually do with the tools we already have. We're past the "look what ChatGPT can do" phase and into the "okay, now what?" phase. And that shift matters more than most people realize.

The infrastructure is getting serious. Companies are spending billions on data centers built specifically for AI workloads. That's not hype money—that's bet-the-company money. When you see that level of capital investment, you're watching an industry move from experimentation to industrialization. The interesting question isn't whether AI will be embedded in our tools, but how quickly the embedding happens and who controls it.

Open source is making this weird. A year ago, the assumption was that AI would be dominated by a few massive players with the resources to train frontier models. That's still partially true, but the open source community keeps releasing models that are "good enough" for most use cases. Meta's Llama models, Mistral's work in Europe, various research labs—they're all pushing capable models into the wild. This creates a strange dynamic where cutting-edge AI is simultaneously a tightly controlled resource and something you can run on your own hardware.

The practical applications are where things get messy. AI coding assistants are genuinely useful but also train developers to accept code they don't fully understand. AI writing tools help with the blank page problem but raise questions about what writing even is when the first draft comes from a model. Customer service bots can handle routine queries but make it nearly impossible to reach a human when you need one. Every benefit has a corresponding downside that we're just starting to reckon with.

The regulation question is coming to a head. Europe has its AI Act, various U.S. states are proposing their own rules, and China has been regulating AI for years. But the tech moves faster than policy, and the policy moves faster than most organizations can adapt. We're heading into a period where the rules are unclear, compliance is complicated, and the penalties for getting it wrong are potentially severe.

Here's what I'm watching: how AI affects the economics of creative work. Not the philosophical question of whether AI can be truly creative, but the practical question of whether people can still make a living doing creative work when AI can produce acceptable output for near-zero marginal cost. We're running an experiment on what happens when you flood the market with cheap substitutes for human labor, and I'm not sure we're prepared for the results.

The smart approach right now is strategic experimentation. Try the tools. Understand what they're good at and where they fall short. Build fluency with AI capabilities the same way you'd learn any other professional skill. But stay skeptical. Question the outputs. Understand the limitations. And remember that just because AI can do something doesn't mean it should, or that doing it that way is the best option.

The technology isn't going away. The question is whether we shape it or let it shape us.

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