Many of the curious ones will be adversely affected.
When you're a college student, the stakes feel so high. You have to pass this class or else you'll have to delay graduation and spend thousands of dollars. You have to get this grade or else you lose your grant or scholarship. You want to absorb knowledge from this project (honestly! you really do) but you really need to spend that time studying for a different class's exam.
"I'm not lazy, I'm just overwhelmed!" says the student, and they're not wrong. But it's very easy for "I'm gonna slog through this project" to become "I'm gonna give it a try, then use AI to check my answer" and then "I'm gonna automate the tedious bits that aren't that valuable anyway" and then "Well I'll ask ChatGPT and then read its answer thoroughly and make sure I understand it" and then "I'll copy/paste the output but I get the general idea of what it's doing."
I was being sarcastic there, a bad habit of mine. There are some advantages to being an early adopter (you get to reap some of the benefits now), but it doesn't give you a permanent advantage, and the people who aren't closely following and adopting weeks-old tools aren't doomed to irrelevance.
The iPhone was an equalizer. Existing mobile devs did get a genuine head start on mobile app design, but their advantage was fleeting.
I do use these tools though! I spent some time with AI. I have coworkers who are more heads-down working on their projects and not tinkering with agents, and they're doing fine. I have coworkers who are on the absolute bleeding edge of AI tools, and they're doing fine. When the tooling matures and the churn lessens and the temperature of the discourse is lowered, I'm confident that we will all be doing great things. I just think that the "anybody not using and optimizing Codex or Claude Code today is not gonna make it" attitude is misguided. I could probably wring out some more utility from these tools if I spent more time with them, but I'd rather spend most of my professional development time working on subject matter expertise. I want to deeply understand my domain, and I trust that AI use will (mostly) become relatively easier to pick up and less of a differentiator as time goes on
If the "meta" changes so quickly, then that sets an upper bound as to how far behind you are, no? Unless you are doing low-autonomy, non-specialized work or are applying to fly-by-the-seat-of-your-pants startup jobs, no hiring manager is going to care if you have three months less experience with Codex than the other candidate.[1]
> so much of using LLM right now is context management
That is because the tooling is incredibly immature. Even if raw LLM capabilities end up plateauing, new and more effective tools are going to proliferate. You won't have to obsess over managing context, just like we don't have to do 2023-level tricks like "you are an expert" or "please explain your thought process" anymore. All of the context management tricks will be obsolete very soon... because AI tooling companies are extremely incentivized to solve it.
I find it implausible that the tech is in a state where full-time prompters are gaining a durable advantage over everyone else. J2ME devs probably thought they were building a snowballing advantage over devs who dismissed mobile development. Then the iPhone came out and totally reset the playing field.
[1] Most employers don't distinguish between three months and nine months of experience with JS framework du jour, no matter what it says on the job listing
Edited to add: Claude Code brought the agentic coding trend to the mainstream. It came out three months ago. You talk about how much you're laughing at the naivete of people here, but are you telling me with a straight face that three months is enough to put a talented engineer "behind"? At risk of being unemployable? The engineers who spent the last three months ping-ponging between Claude Code, Cursor, Codex, etc. can have their experience distilled into like a week of explaining to a newcomer, and I predict that will be true six months from now, or a year from now.
If progress continues at the rate that AI boosters expect, then soon you won't have to use them smartly to get value (all existing workflows will churn and be replaced by newer, smarter workflows within months), and everybody who is behind will immediately catch up the moment they start to use the tool.
I can't think of many engineering disciplines that do things this way. "This seems to work, I don't know how or why it works, I don't even know if it's possible to know how or why it works, but I will just apply this moving forward, crossing my fingers that in future situations it will work by analogy."
If the act of discovery and iterative refinement makes prompting an engineering discipline, then is raising a baby also an engineering discipline?
I think we agree. Interacting with employees is not an engineering discipline, and neither is prompting.
I'm not objecting to the incantations or the vibes per se. I'm happy to use AI and try different methods to get the results I want. I just don't understand the claims that prompting is a type of engineering. If it were, then you would need benchmarks.
How do we benchmark these different methodologies?
It all seems like vibes-based incantations. "You are an expert at finding vulnerabilities." "Please report only real vulnerabilities, not any false positives." Organizing things with made-up HTML tags because the models seem to like that for some reason. Where does engineering come into it?
> If you cannot afford the hundreds of dollars a month Claude costs
Employers will buy AI tools for their employees, this isn't a problem.
If you're saying that you need to buy and learn these tools yourself in order to get a job, I strongly disagree. Prompting is not exactly rocket science, and with every generation of models it gets easier. Soon you'll be able to pick it up in a few hours. It's not a differentiator.
I keep hearing people express dismay that people are financing their lunch, sneakers, or concert tickets.
But this has been the norm for a while, no? I can't remember the last time I didn't utilize credit at a restaurant or retail store. If you use credit cards, it doesn't make sense to reflexively admonish people for using BNPL for everyday purchases.
To be sure, BNPL is in many ways a predatory innovation. But it isn't totally novel. It seems like a natural consequence of what came before.
> I get paid to provide technical solutions to business problems.
That's true of all SWEs who write HTML and CSS, and it's the reason I don't think there's much downside for devs to not proactively start using these agentic tools.
If it truly turns weeks of work into hours as you say, then my managers will start asking me to use them, and I will use them. I won't be at a disadvantage compared to people who started using them a bit earlier than me.
If I am looking for a new job and find an employer that wants people to use agentic tools, then I will tell the hiring manager that I will use those tools. Again, no disadvantage.
Being outdated as a tech employee puts you at a disadvantage to the extent that there is a difficult-to-cross gap. If you are working in COBOL and the market demands Rust engineers, then you need a significant amount of learning/experience to catch up.
But a major pitch of AI tools is that it is not difficult to cross the gap. You draw on your domain experience to describe what you want, and it gives it to you. When it makes a mistake, you draw on your domain experience to tweak or fix things as needed.
Maybe someday there will be a gap. Maybe people will develop years of experience and intuition using particular AI tools that makes them much more attractive than somebody without this experience. But the tools are churning so quickly (Claude Code and Cursor are brand new, tools from 18 months ago are obsolete, newer and better tools are surely coming soon) that this seems far off.
I actually agree with this. I use LLMs often, and I don't compare them to a calculator.
Mainly I meant to push back against the reflexive comparison to a friend or family member or colleague. AI is a multi-purpose tool that is used for many different kinds of tasks. Some of these tasks are analogues to human tasks, where we should anticipate human error. Others are not, and yet we often ask an LLM to do them anyway.