While I understand the desire to reassure developers, I think this perspective seriously underestimates the pace of progress in AI. Just 3-4 years ago, the idea of AI writing any functional code seemed far-fetched. Now they can handle many coding tasks competently.
The author lists specific tasks LLMs can't do today. But there's no fundamental reason they won't be able to in the near future. Domain expertise, understanding downstream effects, configuring CI pipelines - these are all learnable patterns. As models get larger, are trained on more diverse datasets, size of context window increases, and new architectures emerge, these capabilities will come online rapidly. The jump from GPT-3 to GPT-4 was substantial, and we should expect continued leaps.
This doesn't mean human developers will become obsolete overnight. But it does mean the nature of software development work is likely to change significantly. Lower-level coding tasks may be increasingly automated, shifting focus to higher-level design, architecture, and problem framing.
Rather than dismissing the potential impact, we should be preparing for a world where AI significantly augments or even replaces many current development tasks. This might involve focusing more on skills that complement AI capabilities or exploring new areas where human creativity and insight remain critical.
current AI is already stressing the power grid, and much of it will need to be redeveloped and improved just to keep pushing the limits of LLM’s. Power is the limiting factor with scaling here, so i’m rather unconvinced with your hypothesis. The improvements in the last 2-3 years are in no way indicative of the next 2-3 years.
I agree with your sentiment by the way, developers should find ways to use LLM’s to improve their development process. But the drama is getting old.
By swiping you force people to ‘rate’ everyone they see. You can then use a method like the Elo rating system to match musicians of similar skill levels (or desirability?) more easily.
Privacy.com - let's you easily make virtual burner cards that you can use for free trials and not worry about having to cancel the subscription on time.
Please be aware that while this might (!) work in the US, in many other jurisdictions not canceling a subscription makes you still liable to pay, even if your card is gone; welcome to the wonderful world of debt collection.
I have been working on LLM projects for the past 2 years and I still share a similar anxiety to you. Will what I am building just be a small added feature inside a future ChatGPT or will an AI just be able to do it out of the box in the future?
However, I don't think that is a reason to not build - if anything it is a reason to build faster and just believe that you will be able to adapt to whatever happens in the future. And on a personal note, a part of me envies people who aren't actively working on projects using LLMs -- you guys are the ones who are more likely to stumble across unique problems to solve with AI that most of us just didn't see.
I agree that a lot of crypto grifter seem to be moving on to AI. But it seems like the author is doing the same thing. Since no one is interested in reading his criticism of cypto anymore, he is now moving on to AI. Legend.
I don't think he's moving to AI. It's a whole lot harder to mass scam people in AI than in crypto. BTC and ETH are still pretty high in price. He's still only covering crypto. But he's right that many scammers, grifters, opportunists have moved to AI.
Even the ones left in crypto are basically trying to associate their crypto projects with AI as much as possible.
If real estate was hot, crypto would try to associate with real estate. If room temperature superconductors are invented, they'd say crypto for superconductors. Basically, in order for useless crypto shitcoins to get attention, they attach themselves to anything that's hot.
co-author here: lol I assure you we have no wish to continue on this AI path. OTOH, we both got new patrons all of a sudden. And there's a similar article to write about quantum computing grift, with massive misselling of systems that might one day be able to factor numbers as high as 35.
Hi Michael, I am a UC berkeley student and along with a few friends we have made a daily newsletter service (www.wokenews.co).
As a bipartisan political newsletter whose primary user base right now are college students in Berkeley, would you have any recommendation on how to expand beyond this bubble of student subscribers? Maybe on how to reach out to influencers who may be willing to help?
The author lists specific tasks LLMs can't do today. But there's no fundamental reason they won't be able to in the near future. Domain expertise, understanding downstream effects, configuring CI pipelines - these are all learnable patterns. As models get larger, are trained on more diverse datasets, size of context window increases, and new architectures emerge, these capabilities will come online rapidly. The jump from GPT-3 to GPT-4 was substantial, and we should expect continued leaps.
This doesn't mean human developers will become obsolete overnight. But it does mean the nature of software development work is likely to change significantly. Lower-level coding tasks may be increasingly automated, shifting focus to higher-level design, architecture, and problem framing.
Rather than dismissing the potential impact, we should be preparing for a world where AI significantly augments or even replaces many current development tasks. This might involve focusing more on skills that complement AI capabilities or exploring new areas where human creativity and insight remain critical.
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