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This is kind of a "I'm not here to tell you about Jesus he either lives in your heart or he doesn't". I am an engineer, I have about 15 years of experience. I

This is kind of a "I'm not here to tell you about Jesus he either lives in your heart or he doesn't". I am an engineer, I have about 15 years of experience. I have been using AI in my job since mid 2025. Over that time it has gone from being an interesting toy that could kind of help but would often hinder, to being an absolutely explosively powerful tool. Just from personal experience it is the thing that has improved the most of any of my tools in career. And over that time my spend on AI has sky rocketed. You don't have to believe me, it's obviously just anecdotal, but for anyone in the same position as me (And there really are lots of us), to claim model capability peaked in 2024 is just staggeringly dumb. It'd be like claiming electric cars peaked in 2008. I don't know how further to convey this to you. It may very well be the case that the financial side is a bubble that horribly bursts. But the technology is real and the claims Ed has made are just wrong.

fyredge 15d ago
Don't worry about Jesus, he's fine. I'm the same as you sans 10 years of working experience, with similar experiences using LLMs. The distinction I would like to make is that it's not the models that are improving, but rather the infrastructure around them. It started with prompt engineering, then chain-of-thought, then mixture-of-experts, now harness engineering etc. These, I think are what's driving LLM complexity, not the model. To bring back to your electric car analogy, the electric motor peaked early on, but was not quite useful as a car until advancements in battery density, charging technology, regenerative braking were made. These are all the infrastructure needed to improve the viability of owning electric cars, just like the infrastructure was needed to make the models useful. To be honest, I'm much more excited to see development of infrastructure around local models than seeing the arms race between AI labs. At least the former benefits the end user in a transparent way.

fyredge 15d ago

Don't worry about Jesus, he's fine. I'm the same as you sans 10 years of working experience, with similar experiences using LLMs. The distinction I would like to make is that it's not the models that are improving, but rather the infrastructure around them. It started with prompt engineering, then chain-of-thought, then mixture-of-experts, now harness engineering etc. These, I think are what's driving LLM complexity, not the model. To bring back to your electric car analogy, the electric motor peaked early on, but was not quite useful as a car until advancements in battery density, charging technology, regenerative braking were made. These are all the infrastructure needed to improve the viability of owning electric cars, just like the infrastructure was needed to make the models useful. To be honest, I'm much more excited to see development of infrastructure around local models than seeing the arms race between AI labs. At least the former benefits the end user in a transparent way.