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Comment by bigyabai | Hacker News Reader

I don't think either of you are wrong. The parent's assertion is that we've known this for almost a decade. BERT was highly usable for classification and sentiment analysis a whopping 9 years ago, despite being less than 0.5B parameters large. Similar-scale models like FLAN-T5 showed that it could be improved without substantially scaling up.

Today, we're extremely spoiled by trillion parameter-scale models. Our conceptualization of vibe coding relies on wasteful tool-calling paradigms, the one-size-fits-all mentality of LLMs is part of the marketing blitz to make people buy more tokens. It's lazy on the part of frontier labs, but also wastes electricity, time and money.

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Lol your argument is the same as programmers who complain about Javascript and internet browsers being the most common interface for all solutions on a computer

You guys dont understand that the Lowest common denominator ALWAYS wins - its why excel is the linga franca for most companies

LLMS and AI coding are the new javascript easy way to build amazing things and that trumps the tool specializers

Years of Big Data and Data Engineers building fit for purpose ML pipelines expensively working in a shadowy corner of the company have been replaced by the PM vibe coding a tool to categorize his emails by relevance

Again - you are right, but it still doesn't refute the grandparent's claim that today's AI is lazy, wasteful and marketing-driven. There is room to improve, and if US labs don't take the initiative then Chinese ones will.

It is lazy and wasteful if you ignore the costs of specialized skills in doing it the "right way." If you stop looking at things in a narrow technical frame, and look at it as an organization, it's not wasteful. And lazy is a useless pejorative used against products that let people do things easily. Lazy is good. When you learn how to make products that allow people to be more lazy, you will become successful.

What results though. The people seeing measurable improvements to their core work with LLMs are coders.

Everyone else is taking over intern level work from someone else’s team. They are reducing the friction costs of talking to someone else, for about a 30% productivity gain.

Firms are trying desperately to automate their white collar workers, and that is following the same trend as all other automation projects, and ML/deep learning efforts in history.

There's no need for black and white thinking. Javascript and the internet browser are the most common interface sure, but there's still room for specialised desktop software, especially those that require serious performance like anything to do with 3d graphics or real-time audio.

But also, frontier LLMs are enormously expensive and slow. Using Astra for things like simple text classification is not going to scale, and you're likely to end up in the same boat as those people who saw their Vercel bill shoot up to $96k/week when their site got traction, if not worse.

A personal saying of mine: In computers the second best thing always wins.

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