The Year of AI Optimization

When we first began seeing a real boom in artificial intelligence (AI) two years ago, we were all experimenting with AI; we would try out all of the smartest, most costly AI models on every conceivable problem just to see how much we could accomplish. This was an excellent time to show that AI could provide people with more time, generate brilliant ideas, and transform their working methods.

However, things have changed since then. Based on what you see in your current software stack, it is likely that your AI subscription costs are accumulating rapidly. You may have subscriptions for premium-quality chat interfaces, advanced image-generating products, and corporate-grade programming assistants.

It's time to face the music: This year is the year of optimization.

You no longer need to use the absolute best, most expensive model for every single task on your to-do list. In fact, if you're still doing that, you're throwing money down the drain. The secret to winning with AI right now isn't about having access to the smartest model; it's about getting an acceptable, high-quality outcome at the cheapest possible cost.

The "High-End Performance Vehicle for a Trip to the Grocery Store" Analogy

Driving a high-performance sports car (say, a Ferrari) around town just to get one carton of milk at a grocery store is probably more costly and inefficient than driving a more economical vehicle that would essentially accomplish the exact same task.

If you use a frontier-level AI (such as Claude Opus or the highest tier of ChatGPT) to accomplish common tasks such as generating timestamps for a video or formatting an easy list, you’re doing exactly that. Let’s take a look at a simple real-life example from an everyday content creation process: You’ve just finished creating a new piece of content and now you need:

- A catchy title idea

- A short punchy thumbnail text

- Timestamps for your viewers

- Short viral segments you can clip and post as shorts

Your initial thought may be to take the transcript and give it to the highest-end AI you have access to, but that’s unnecessary considering you could use a low-cost, efficient model such as DeepSeek that can do this type of work very accurately for roughly 1/10th the cost of high-end credits.

Aligning Task & Cognitive Ability

To make your workflow work better, categorize daily tasks according to their cognitive "heavy lifting" requirements.

Tier I: High-Level Cognition (Frontier Models)

Use the most intelligent model available for tasks requiring a high degree of cognitive input (complex code structures, creating long-term strategic plans, highly elaborate creative writing, analyzing large sets of data for hidden patterns).

Tier II: Day-to-Day Processing (Cost-Effective Models)

Routine operational tasks performed daily (summarizing documents, extracting time stamps, basic translation services, preparing basic correspondence via e-mail, or creating metadata) do NOT require an exceptionally intelligent AI, so use a less expensive and faster model to complete (provide identical results).

Constructing Individual Workflows To Generate Higher Levels Of Efficiency

One of the easiest ways to implement this new mindset of optimization is through the building of customized applications (apps) or creating automated processes (workflows) for your repetitive task.

Let me go back to our previous example of content creation: rather than manually putting in the prompt into a premium AI after completing a project, I now have built a simple custom tool that when I input my transcript into the program, it is already configured (pre-set) to ping the low-cost model of the AI to produce the same output as the premium AI generates by giving me title, thumbnail suggestions, timestamped segments of my video and viral video clips.

Thus, I still receive the exact same output, but it will all be automated and without risking any of my premium AI usage credits.