I've recently had an interesting discussion on AI usage by students with a couple of friends who are all professors or lecturers in different fields. They report that AI makes their job harder, especially because it widens the gap between students:
Students who would have straight up failed before now produce passable essays/projects, but do not understand what they are handing in. They outsource their thinking and are overall worse than before. Students who would have excelled already pre-ai, are now even better. They use AI to learn more effectively, do broader research, and have more time to do deep research because they can outsource the grindy stuff.
This is very similar for software engineers. I'm not much faster at implementing features than before AI, despite heavily relying on agentic coding (95% of code is ai generated), but I've had significantly fewer bugs in production, less feature change requests (because w spend much more time designing/experimenting), and we are able to drive-by refactors or vulnerability fixes we would have grudgingly ignored before.
My coding skills are atrophying, but that was never interesting to me anyway. I'm getting better at systems design, cyber sec, data modelling and requirements engineering, because I have much more time to spend on it now. If LLMs disappeared tomorrow, I'd have to relearn coding, but I think it is significantly more likely that hand coding will become a specialist niche for experts.
I had an Ethics professor in uni who would ask his intro classes to raise their hands if they thought they were a morally upright person. On average, much fewer philosophy majors would raise their hands, and they would continue to decrease throughout their studies.
Of course, the thought process of "I'm trying my best, but sometimes failing", could push someone in both directions of the answer, but striving to be better only works when honestly acknowledging failures. In my experience, the majority of people tend to construct their moral views conveniently along the lines of their preferred actions, but principles built on convenience usually don't hold up to scrutiny.
This only really matters in a world where Prompt Injection and Jailbreaking isn't trivial in the first place though. All current models are still extremely exploitable.
I strongly suspect we are only scratching the surface of activation engineering at the moment, and there's plenty of very targetted ways of lobotomizing or cracking LLMs if you understand the model in detail.
I suspect that Grok has been ironically lobotomized by pressures to correct its political views.
Similarly, I could imagine the Gemini folks working in a significantly more complex corporate climate, with different parts of Google pushing for different capability focuses. They are only lagging behind less than a year, so it isn't too large of a gap yet.
That said, the fact that Anthropic is currently the top dog suggests that talent and execution is incredibly important. A year ago none of my normie friends new them, and when i suggested using Claude looked at me like when I recommend Linux.
I would argue a different case: When AI is noticeable, it is obvious the person doesn't care enough to bother with you.
I'm a heavy user of AI, but it has not changed the amount of time I invest in certain things, rather, I think it has unequivocally raised its quality. In some cases it has also raised the ceiling at how good I can do things, so I'm investing more time, effort and creativity now.
For example: I'm currently working on a workshop on AI security. This is obviously time boxed by all my other obligations, and I can spend maybe 60 hours to prepare it, hopefully in a quality that my captive listeners don't have to suffer.
The newest models provide me with at least 3 major benefits:
- I have merciless feedback and criticism always available. A lot of it I might disagree with, but I'm quick to dismiss. Same with ideas. If 9/10 ideas are bad, but I'm able to dismiss them quickly, I got one good idea quicker than I can take a shower.
- I can research much more broadly by querying LLMs than just consulting Google et al. A lot of interesting minor breaches, blog posts, or forum discussions I would have missed without letting Claude and Gemini run deep research sessions.
- I can quickly generate fictional scenarios and examples. In that workshop I would like to go through some scenarios on risks of using AI Coding Agents. Creating those scenarios in the past would have cost me days, and now it is a matter of an hour or two.
My coding, writing and general life-planning has equally benefited from LLMs. I've successfully planned a vacation with Claude while on the flight to that vacation, and I've not had to fix a major bug in my software in 3 months. I'm not moving a lot faster, but I'm moving a lot more deliberately.
> In my long career I've noticed a strong correlation between SAT scores and academic performance as well as job performance
On average. However, I've also had the experience that some of the most competent people I've known had rather difficult teens and twenties.
Hiring someone who flunked out of highschool, worked odd jobs for 10 years, then got a diploma and a degree is higher risk, higher reward. They are often times harder workers, unusual thinkers and more grateful for what they have.
And the soccer WC went the opposite direction, by encouraging scalping, giving it an official avenue, and taking a cut of the profits. Now only rich people get to enjoy a sport meant for the masses, yay.
Reddit was already heavily astroturfed before AI. There's no space on the internet where you can get as much bang for a buck with an influence campaign due to its centralized frontpage. The most obvious example is /r/worldnews, with millions of readers, a few thousand commenters and maybe 40k voters. To skew any discussion, you need at most 6k accounts, and that's giving you the kind of influence on American politics as would the frontpage of the NYT. You could hire real people for each account, and it would still be worth it.
History does not repeat itself, but it rhymes. Drawing these comparisons to the Dotcom bubble is only of limited utility. I think there's good reason to believe that recursive self-improvement is a bust, and LLM models will become a commodity. The real value lies in multi-modal integration and good harnesses. The current frontier labs are theoretically in a good position to capitalize on this, but it is far from obvious that they will succeed. I think Google and some of the chinese giants are in a far better position to actually go the last mile.
I mean if we're getting really serious about ai-first development, we might be able to get away with a ton of micro services written with clear, simple apis to communicate all in a giant mono-repo managed by an army of agents. That way the full surface any one agent has to touch is small, compile time of each individual service is very fast, and services can be hand-written if super critical.
Anyway, I've played around with the idea a bit so far, and it seems that current agents/harnesses use way more tokens with that architecture.
There's a difference in thought here so fundamental that it is hard to argue about details without addressing it first: You believe the individual comes first, what it produces is taxes hy the external construct of the state, which takes from what the individual truly earns.
But the individual would be a subsistence farmer at best without society supporting it, hardly worth taxing. In reality millionaires are only possible with a large state and societal network around them. Wealth is societies gift, and its right to take back.
I love their product and use them myself. But where's the value proposition for investors? Unless they get purchased by one of the large cloud providers, they will get pushed out of the market sooner or later.
What's the value proposition for the typical AWS startup to go with openrouter, if Amazon offers similar rates with direct integration into all their other offerings?
The only reason OpenRouter can exist at the moment is because we are in the wild-west phase of this technology, and lots of people and companies are exploring. In 5 years they will have to have transformed their business fundamentally, or go the way of the dinosaurs.
The fairest and easiest to realize wealth tax is on inheritance. It is great to want to give your kids a headstart in the world, it is terrible for them and the people around them to set them up for life.
The article itself isn't great, but it speaks to one of my greatest concerns about AI. People who engage heavily with it are falling in the behavioral billionaire trap: It is deeply unhealthy to be constantly affirmed in your behaviors. No, not all of your ideas are great, not everything you say has value. You are not a cut above the rest.
There are enough stories of people completely losing the plot, thinking they've invented a new type of maths or similar, but there's almost certainly also a much more subtle influence in most of us, where the constant affirmation, obedience, apologia, reframes our expectations of how interactions should be.
We are already the most narcissistic generation, having been molded by social media to compare, stats-max, and overobsess about who we are. Chatbots are now fanning the flames.
With the rise of agentic coding, this has become a sign of quality for me in my own PRs and reviews: New features implemented in less than a thousand lines of productive code.
When I'm working on code that was heavily vibecoded, most of my PRs are reducing LoC by a couple hundreds of lines while fixing bugs or implementing a new feature.
My job kind of feels like being a garbage man, luckily my current employer appreciates it. Personally I think the current style of vibecoding only kinda works, because models are getting better fast enough to keep the shitpile from overflowing completely. Betting on the harnesses + models getting good enough to clean up after themselves is a bet, and I don't like gambling, but even I admit the odds don't seem to be bad.
Could you maybe in brought strokes explain what you are working on? I think it is very plausible that the disconnect is between people writing front ends/rest apis vs people solving things like graphics.
I tend to agree. Taking shortcuts are one thing, not daring to refactor along the way another. I would only do this in low stress situations due to the risk of producing new bugs or issues, and just lacking the time to properly update tests etc. Opus 4.7 sometimes makes suboptimal design decisions, especially in terms of overcomplicating things, but I have not seen it produce an actual bug in smaller changes in a long while.
The other is using Agents as critical reviewers. I've let Opus 4.7 review PRs by very senior people. Most of the suggestions are meh, but usually there's at least 1 or 2 that improve the code base unequivocally.
Also there are still considerations like domain, team expertise, org ecosystem etc. to consider. I love to use Rust for most things, but now I'm working with an org that primarily has expertise in Java, and I'm not going to rock the boat for barely any reason. Python is also still useful for most ML stuff, and Django is quite a pleasure to work with (although it wouldn't be my first choice).
The great thing about LLM-assisted coding is that an experienced software engineer can acquire decent familiarity with a language quite quickly. And then has a useful sparring partner for understanding and using the quirks and features of a new language.
Students who would have straight up failed before now produce passable essays/projects, but do not understand what they are handing in. They outsource their thinking and are overall worse than before. Students who would have excelled already pre-ai, are now even better. They use AI to learn more effectively, do broader research, and have more time to do deep research because they can outsource the grindy stuff.
This is very similar for software engineers. I'm not much faster at implementing features than before AI, despite heavily relying on agentic coding (95% of code is ai generated), but I've had significantly fewer bugs in production, less feature change requests (because w spend much more time designing/experimenting), and we are able to drive-by refactors or vulnerability fixes we would have grudgingly ignored before.
My coding skills are atrophying, but that was never interesting to me anyway. I'm getting better at systems design, cyber sec, data modelling and requirements engineering, because I have much more time to spend on it now. If LLMs disappeared tomorrow, I'd have to relearn coding, but I think it is significantly more likely that hand coding will become a specialist niche for experts.