You will start seriously worrying about coding AI bills within 6 months

once upon a time - engineers often had to concern themselves with datacenter bills, cloud bills, and eventually SaaS bills. We'll probably have 5-10 years of being concerned about AI bills before the AI expense is trivial compared to the human time.

I think two things can be true simultaneously:

1. LLMs are a new technology and it's hard to put the genie back in the bottle with that. It's difficult to imagine a future where they don't continue to exist in some form, with all the timesaving benefits and social issues that come with them.

2. Almost three years in, companies investing in LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them, the majority of consumer usage is at the free tier, the industry is seeing the first signs of pulling back investments, and model capabilities are plateauing at a level where most people agree that the output is trite and unpleasant to consume.

There are many technologies that have seemed inevitable and seen retreats under the lack of commensurate business return (the supersonic jetliner), and several that seemed poised to displace both old tech and labor but have settled into specific use cases (the microwave oven). Given the lack of a sufficiently profitable business model, it feels as likely as not that LLMs settle somewhere a little less remarkable, and hopefully less annoying, than today's almost universally disliked attempts to cram it everywhere.

> No, it's not. It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). But it's not like GDB or using UNIX as a IDE where you need a whole book to just get started.

The skill floor is something you can pick up in a few minutes and find it useful, yes. I have been spending dedicated effort toward finding the skill ceiling and haven't found it.

I've picked up lots of skills in my career, some of which were easy, but some of which required dedicated learning, or practice, or experimentation. LLM-assisted coding is probably in the top 3 in terms of effort I've put into learning it.

I'm trying to learn the right patterns to use to keep the LLM on track and keeping the codebase in check. Most importantly, and quite relevant to OP, I'd like to use LLMs to get work done much faster while still becoming an expert in the system that is produced.

Finding the line has been really tough. You can get a LOT done fast without this requirement, but personally I don't want to work anywhere that has a bunch of systems that nobody's an expert in. On the flip side, as in the OP, you can have this requirement and end up slower by using an LLM than by writing the code yourself.

If you are resource constrained this article will make you sad.

I do not have unlimited funds to plug in some token and burn a bunch of money when writing code.

I am gpu poor. I'm lucky that 8gb vram can run the smallest models. But the output is so poor that I lose out to anyone using a hosted service.

If anything this article shows that building great programs is less democratized than it once was.

The cost of sharing your code is unknown, though.

> So he’s going to selflessly donate it to himself to save himself from taxes? What a hero.

You don't really "save yourself from taxes" by donating money to charity.

Option A: sell stock for $100, pay taxes of $20, spend $80 on yourself Option B: donate stock of $100 to charity, and spend $0 on yourself

Which of these options leaves Gates with more money in his pocket to spend on himself?

Giving money away doesn't save you from taxes on your income; you just forego the income entirely. The money is gone. It's no longer yours. Why would you be paying taxes on it?

It seems like these discussions always go the same. "Leverage your existing reputation and contact list". That is completely missing what the OP asked.

How do you start from nothing?

As someone who is often on SF city streets without a car - I bike and run a lot - I absolutely love Waymo. I am continuously seeing human drivers cut me off, perform illegal maneuvers (i.e. run red lights when I'm going through a crosswalk), and break various other traffic laws. All these things genuinely put people in danger. Just the other day, a guy started running a "no right turn on red" lane in SF, and when I pointed it out to him he floored his car - through the red - right in front of me and laughed at me as he sped away. To say nothing of all the times when cars will honk or give me the finger for doing normal things on a street, like walking on a crosswalk.

Waymo is like the most courteous, respectful driver you can possibly imagine. They have infinite patience and will always take the option which is the safest for everyone. One thing which really impressed me is how patient they are at crosswalks. When I'm jogging, a Waymo will happily wait for me to cross - even when I'm 10 feet away from even entering the crosswalk! I don't know if I even have that much patience while driving! I've had a number of near misses with human drivers who don't bother checking or accelerate for no reason after I'm already in the crosswalk. Can you imagine a Waymo ever doing that?

If I see a Waymo on the street near me I immediately feel safer because I know it is not about to commit some unhinged behavior. I cannot say enough good things about them.

Great seeing Ray Mooney (who I took a graduate class with) and Emily Bender (a colleague of many at the UT Linguistics Dept., and a regular visitor) sharing their honest reservations with AI and LLMs.

I try to stay as far away from this stuff as possible because when the bottom falls out, it's going to have devastating effects for everyone involved. As a former computational linguist and someone who built similar tools at reasonable scale for largeish social media organizations in the teens, I learned the hard way not to trust the efficacy of these models or their ability to get the sort of reliability that a naive user would expect from them in practical application.