Save it for the Whitepaper: What Actually Matters for Maintaining SOTA AI
Each week, a new model drops, a benchmark gets broken, and a new AI startup launches to lead us to the future. You can hardly download the weights fast enough to verify all the claims - let alone run a costly, time-consuming evaluation harness for each new candidate model.
With the low-hanging fruit of general intelligence gains from pure data and model scaling largely exhausted, model providers are pivoting toward amplifying smaller advantages that distinguish their AI. Lacking the discipline to stick to an apples-to-apples comparison, some apply hacks to boost their reported performance metrics.
Given the proliferation of specialized benchmarks, it's not hard for model providers to find a few for which their models excel to frame a favorable comparison. It's just another manifestation of the curse of dimensionality: with enough benchmarks, you'll always look good on a few. More insidiously, there have been instances of model providers training directly on evaluation data.
While unprincipled development practices can quickly generate publicity, they eventually undermine trust. Inevitably, the community uncovers a model's true capabilities and limitations, finding edge cases in the wild even faster through virality.
Until model providers offer greater transparency about training data composition, reliably qualifying a model's capabilities will remain an elusive industry challenge.
Indeed, boring old documentation might be the real accelerator while helping make the AI content overload manageable.
Alas, when AI fails to meet production expectations, you probably won't get to blame the vendor for overpromising. That's why starting with a clear definition of the success metrics you can trust to align AI for your application is essential.
New providers enter the space weekly as the cost of making AI continues to fall, so you can expect to miss opportunities by simply partnering exclusively with a few trusted incumbent vendors.
How can you practically keep up with the state of the art to serve the best AI for your users?
Aside from recognizing hype around research benchmarks in AI, understand that the research community exalts technical novelty. Unfortunately, novelty is often undesirable when building robust production AI systems. For instance, the cost of running that flashy new architecture may be sacrificing engineering fundamentals like efficiency without the highly optimized kernels of your favorite inference engine.
Features making for reliable production AI often aren't what we celebrate in trending whitepapers. However, for many applications, low latency inference matters much more to users than a few points on a research benchmark task. That fancy new attention mechanism might justify branding a new architecture, but will it move your business metrics any?
Using simple, low-cost techniques like parameter-efficient finetuning to customize your models, you can explore an optimal speed-accuracy tradeoff, even beating frontier performance for your application.
Distillation is another powerful method for bridging research capabilities with practical engineering considerations by boosting the capabilities of inference-optimized architectures learning from larger, more capable ones.
In theory, it's exciting to hear about that new AI crushing your domain's most relevant benchmarks. In practice, there are many other considerations before you can put that model to its ultimate test: production deployment.
If it's worth deploying, it's worth maintaining. In your search for the best-performing AI, you'll explore more efficiently by comparing your experience against insights from the broader research community — separating what works reliably from what looks good on paper.