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Right now, speed is everything in AI research.

Haebom
They say in grad school that good research comes from putting in the hours at your desk, but these days, that doesn't seem to be the case in AI research. The speed of AI research has only grown in importance over the past two years. This speed isn't just about moving quickly, but about producing efficient and high-quality research within a set timeframe. This principle is especially crucial given the rapid progress and global competition in the field of AI.
아 엉덩이의 힘이라는게 이런거였나?

Wasn't it slow before?

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AI research revolves around experiments and analysis based on data. By running quick experiments and analyses, you can get fast feedback and improve the accuracy of your models and algorithms.
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AI technology is making new discoveries and steadily advancing every day. For researchers to keep up with this pace, moving quickly is crucial. All around the world, countless researchers are trying to develop new methods and systems in AI. Speed is the decisive factor in staying ahead of the competition.
Because of all this, the AI industry is in dire need of better research environments—in other words, better infrastructure. (Honestly, who wouldn't want to have a fully loaded H100 cluster...) But since not everyone has access to top-notch infrastructure, this is where MLOps and LLMOps come in, both as businesses and as key functions.

This is where I draw the line

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Don't sacrifice quality: Speed in research isn't about sacrificing quality just to go faster. In fact, it's about delivering even better research through steady practice and rapid iteration.
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Doesn't mean you have to rush all the time: It's not about working under constant stress, but about minimizing delays with efficient processes.
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Speed doesn't mean just chasing short-term goals: Real speed considers long-term objectives and sustainability. Even if you move quickly toward your short-term goals, never lose sight of your long-term vision.

I get that it's important, but how do I actually do it?

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Iteration: Once you set your goal, it's crucial to keep iterating and collecting feedback.
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A bigger model isn't always the answer, and we can't just rely on 'prayer meta' at every checkpoint.
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In the end, we need to set up an environment where we can quickly see results and gather feedback, even with smaller or existing models.
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Adaptation: It's important to be able to quickly adapt to changes in AI and pivot when needed.
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This really matters... You need to know when to quickly let go of what you've been working on.
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Sometimes, a method you’ve spent months on gets solved instantly by another model. If you cling to it just because of sunk costs, you’ll be left behind.
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In tech transition periods like this, the best strategy may be to sharpen your core knowledge and try your hand at reproducing new models and technologies.
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Prioritization: Figure out what matters most among your tasks, and set up an efficient order based on that.
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Determination: You need the grit to keep pushing until you hit your goal, even if something unexpected happens.
솔직히 논문이 이만치 쏟아지는데 한 분야만 파기에는... 너무 쉽지 않습니다. 지금이야 말로 특이점의 어딘가...
Whereas in the past, research was about mastering one particular field, lately the tempo has sped up. Especially in AI, this speed-focused approach leads to even better results—not just for individuals and teams, but for the entire field. The nature of AI research is changing fast, and staying fast and efficient is becoming the true key to future and current success.
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