Introduction

Everyone’s obsessing over finding the “best” AI model. But in the fast-evolving world of AI, the “best” is a fleeting title. What’s considered the top model today is easily replaced tomorrow — just ask anyone who’s been tracking the recent release of Google’s Gemini 2.5 model or Claude Sonnet 3.7. Today’s “best” will be dethroned before your next quarterly meeting.

Instead of chasing the ever-changing “best,” businesses should focus on what’s efficient and effective for their specific use case. This isn’t about settling for mediocrity; it’s about understanding and applying a classic Economic principle about aiming for equilibrium — the intersection of Marginal Revenue (MR) and Marginal Cost (MC). Because when it comes to AI models, efficiency is the real gold standard.

The Economics of AI Efficiency

At the heart of Economics is the pursuit of efficiency — achieving the maximum possible output for the lowest possible cost. One of the simplest yet most powerful illustrations of this is the concept of Marginal Revenue (MR) and Marginal Cost (MC).

Imagine a graph where MR and MC intersect. That intersection represents the sweet spot of efficiency. Producing beyond this point means your costs outweigh your revenue; producing below it means you’re not capitalizing on potential value.

Now, apply this to AI. If MR is the value you gain from an AI model and MC is the cost of implementing and operating it, then the goal should be finding that point where value generation is optimized for cost. The magic isn’t in finding the “best” model — it’s in finding the most efficient one for your needs.

For example, if your company needs AI for basic Q&A tasks, you don’t need the absolute latest model. You need one that balances cost and capability to produce satisfactory results. Chasing perfection isn’t just impractical — it’s often a waste of resources.

Why the ‘Best’ AI Model Is a Moving Target

Every few months, a new model steals the spotlight. Today, it’s Google’s Gemini 2.5. Before that, it was Claude Sonnet 3.7. The hype cycle is relentless, and what’s “best” is continually redefined by improved architectures, enhanced training datasets, or specific performance benchmarks.

But here’s the catch: Different models excel at different tasks. GPT-4 seems to be the best at general AI usage (or more recently image generation), while Claude might be more efficient at summarization or writing. Gemini excels when there is a large context window required. The “best” is entirely dependent on what you’re trying to achieve.

If your goal is to build independent AI agents for customer service, you don’t necessarily need the flashiest, most powerful model. You need one that gets the job done effectively, at a reasonable cost. In many cases, choosing a model that’s “good enough” is not only acceptable — it’s optimal.

Focusing on ‘Good Enough’ for Your Business Needs

Efficiency beats perfection, especially when the target is always moving. Instead of chasing the next big release, focus on identifying AI models that are good enough for your current needs.

Consider this: If you’re building independent AI agents, you don’t need to constantly update to the latest and greatest. You need a model that meets your specific requirements effectively, whether that’s handling customer inquiries, generating content, or analyzing data. It’s about practicality, not prestige.

Cost efficiency plays a significant role here, too. The cost of AI models and compute power will continue to drop over time, making AI adoption even more feasible. By focusing on efficiency, you can continually improve and expand your AI use cases without breaking the bank.

Strategic Evaluation and Continuous Improvement

This isn’t to say you should never upgrade. The key is knowing when it makes sense to adopt newer models versus optimizing existing ones.

Ask yourself: Does the new model offer capabilities that meaningfully improve performance for your use case? Or is it simply a flashier iteration that offers diminishing returns?

Over time, models will evolve and can be tested in new areas of your business — but only when it makes strategic sense. Efficiency isn’t about complacency; it’s about smart, calculated growth.

Conclusion

The real takeaway? Efficiency trumps perfection. Being “good enough” doesn’t mean settling; it means achieving maximum ROI with what works now. Just like the intersection of MR and MC, finding that sweet spot is about aligning value with cost — not endlessly pursuing a moving target.

If you’re looking to implement AI or test pilot projects, Woz Digital can help you apply these principles to achieve real, measurable efficiency.