I also think Steam does a great job a hiding it, and the new recommendation page is really great IMO. Other than some generic AAA, it introduced me to really great games I enjoyed based on my play history.
The more content is available, the more curation is important and IMO their algorithm currently does a good job at it.
There are some odd cases like that, but you can always "Ignore" a game and it'll never show up again. That also feeds into Steams curation for you based on your interests.
This is correct, but part of the issue is that it significantly increases token usage costs. Some companies are doing:
- PRD and spec fulfillment review
- code review + correction loops
- security review + corrections
- addl. test coverage and tidying
- addl. type checks and tidying
- addl. lint checks and tidying
- maybe more I haven't listed
And these are run after each commit, so you can only imagine the costs per engineer doing this 10, 20, 50+ times per day depending on how much work they're knocking out.
I think if you were to scale that kind of usage across a reasonable team size, costs would start to add up fast — and possibly beyond the cost of paying another engineer every year, especially if a lot of your teammates are new to AI, or aren't using it efficiently. Of course, it all depends on the appetite of the company.
The other constraint is, for those who are being laid off (maybe because of cost reduction to support an AI budget for a smaller team to use), engineers wanting to expand their skill set and practice these levels of usage + efficiency are effectively unable to with their own funding, making it more difficult to find employment as expectations heighten.
Prior to AI entering the fray, software development was largely free for everyone, allowing anyone with enough time and motivation to build the skills towards gainful employment. As AI becomes more prevalent and expectations around how it's used become higher, fewer and fewer applicants will be able to claim they have the experience necessary because it was out of reach due to costs.
And yet, this is exactly what my last job's engineering & product leadership did with their CEO at the helm, before they laid me off.
They vibe-coded a complete rewrite of their products in a few months without any human review. Hundreds of thousands LOC. I feel sorry for the remaining engineers having to learn everything they just generated, and are now having customers use.
And the problem isn't even the Junior Zoomer devs running circles around seniors. It's the CTO or Engineering VP himself disappearing for a few months and single-handedly consolidating a handful of products into a full rewrite for the company, excluding most of their engineering team from the process, and then laying them off after.
The problem is the CEO pretending to be an engineer and thinking they know better because they can write English prompts and spit out a hideous prototype.
The problem is Product Owners using LLMs to "write code" while their engineering team does zero human review before merging it, because their AI tooling was made solely responsible for code quality. If something's broken, just prompt a sloppy fix full of hidden performance and security bugs that the automated code review step missed.
If you think this is hyperbole, I was recently laid off from a company that did exactly the above.
Then in 2027, it will be product owners replacing the entire engineering team, including the CTO, because they made their system too reliable to justify their employment, while the "thinkers" get to build the product, engineers be damned.
People with real skills they acquired over a lifetime are no longer shaping business. Reckless efficiency towards being average will rule the day.
Not sure why this is getting downvoted, but you're right — being able to crank out ideas on our own is the "killer app" of AI so to speak.
Granted, you would learn a lot more if you had pieced your ideas together manually, but it all depends on your own priorities. The difference is, you're not stuck cleaning up after someone else's bad AI code. That's the side to the AI coin that I think a lot of tech workers are struggling with, eventually leading to rampant burnout.
What would I learn that I don’t already know? The exact syntax and property of Terraform and boto3 for every single one of the 150+ services that AWS offers? How to modify a React based front end written by another developer even though I haven’t and have actively stayed away from front end development for well over a decade?
Will a company pay me more for knowing those details? Will I be more affectively able to architect and design solutions that a company will pay my employer to contract me to do and my company pays me?
They pay me decently not because I “codez real gud”. They pay me because I can go from empty AWS account, empty repo and ambiguous customer requirements to a working solution (after spending time talking to a customer) to a full well thought out architecture + code on time on budget and that meets requirements.
I am not bragging, I’m old those are table stakes to being able to stay in this game for 3 decades
> I am not bragging, I’m old those are table stakes to being able to stay in this game for 3 decades
I'm not old. But slightly past where most would call "young". I see a decent career for the first 5-6 years and then a disruption that completely cuts down on the things you likely had your entire career. Enough time to get a taste of the good times,not enough time to get stable. Meanwhile you buult networks, accomplishments, a seal of trust, in times with overall lower standards.
I don't even use AI but AI has shaped the way I need to navigate the job market and verify knowledge. If you have plenty of pre-AI colleagues and projects to point to you skip all this. But it's a hellscape for people like me.
And I'm sure its an absolute wasteland for new grads
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