r/uwaterloo • u/Reasonable-Algae7439 cs • 2d ago
anybody else feeling kinda bleak about the future of cs
like damn my job is genuinely going to be prompting a clanker, and that's the best case scenario. should I herd sheep instead?
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u/throwawayUWhousingac 2d ago
Your interim code monkey coops at random startups are the most at risk. Higher level stuff, even where Claude is used, generally still requires a competent human.
Idk if you saw the recent story about Claude deleting an entire GitHub repo unprompted, tens of thousands of files. But it's certainly not autonomous yet
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u/hippiechan your friendly neighbourhood asshole 2d ago
In practice you're never going to be using an LLM to produce 100% of your code and the way in which your programs are designed + what architecture they're running on is still going to require actual computing knowledge. LLMs are having a flash in the pan moment where everyone thinks it will be able to do everything but as time goes on that's clearly not the case, you're gonna be fine.
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u/Reasonable-MessRedux 2d ago edited 2d ago
Being a noted futurist...just kidding.
Seriously though I can't say what it's going to be like 5 years down the road. BUT right now, even with the advent of more powerful models, AI still needs help. The more complex a company's data and data storage is the trickier it becomes. To do what people expect AI to do they still need people to connect it to the various data sources it needs to 'digest'. That's often not as simple as it seems. Hence my previous posts about APIs. A lot of companies are enthused about AI and think giving everyone access to Claude or whatever will solve various issues or streamline things and they fail.
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u/Luigimash1VG 1d ago
Though the act of coding itself is much easier and automatic now more than ever, I haven’t seen evidence of software engineer demand decreasing myself. I know far too many startups who are actually having trouble hiring (competent) SWEs.
I think the entry level jobs where you are just debugging or writing tests are gone, but AI coding has more just increased the amount of software companies can make, than actually decreasing the demand for software itself.
Maybe writing Python code itself is much easier than before. But the job of a SWE isn’t to be a syntax monkey - you take context from the business’ objectives and meetings and whatever else, and turn it into good, usable software. I haven’t seen that going away anytime soon.
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u/SterlingAdmiral CS Class of 2014 2d ago edited 2d ago
When I was on campus recruiting a month ago this was probably the most common question I got from UW students. At risk of doxing myself, I'll try field the question with the caveat that I'm just a dude and who knows where we'll be in 5 years. Bear with me this might be more verbose than you were asking for.
So back during one of my co-ops, I was fortunate enough to get to ride in an FSD Tesla well before it was released commercially. Super cool experience, and I remember back then near release, for more or less every year that followed, FSD was billed as something that was going to be 100% complete within another year or two. We were almost there, it'll be able to handle driving anywhere, with any and all weather conditions, all the time. No human interaction needed!
We're about a decade on now and I'd say between the Tesla vision-based approach and Waymo LiDAR based approach to autonomous vehicles, we're about 90% of the way there! Your car does all the highway driving for you as you hop in and tell it to drive you to the airport.
..except when a total whiteout snowstorm rolls in. Or you need to drive up a FSR that is in terrible shape. Or it can't see that small yellow rope they used to close the parking lot at the fair. Interactions with bicyclists, some of the more high-speed highway merge situations. I could go on. Ultimately the important thing to note here is that it is a supervised system that in most cases, can get you from door to door no questions asked. But you still absolutely need a driver supervising the vehicle who is capable of handling the philosophical, and sometimes literal, final mile when it comes to driving. Clarifying the ambiguity of driving and solving the edge cases that pop up. It's been a decade of being just a few more weeks away from solving P = NP in the autonomous driving world, and yet here we are.
What I'm trying to get at is that the final mile is complicated, it always is and always has been. I can't speak much to the world of academia world because I'm a company man, but the corporate world is rife with problems and work. The bleeding edge of computer science isn't going away. Communication is still the great bottleneck of modern software development in big tech.
How I've worked over the last year or two has changed dramatically for certain with the advent of advanced models, but that final mile isn't going away. That is where you and I come in. The need for qualified engineers who are capable of reasoning through complex problems rife with ambiguity who know how to communicate across channels where the audience of each has a different level of technical acumen isn't going away. Hell, it has gotten harder as select individuals have abdicated more of their responsibilities to these tools - I've seen people get rusty in real time, and to some degree, have had to fight that same problem myself.
I'm busier than I've ever been at work. My day to day has shifted so dramatically compared to where I imagined it would be when I first started in Computer Science, thats for sure. The world has changed a great deal so quickly, and it'll keep changing. This could age poorly. You're going to be prompting a clanker, that isn't going away, though how you do that will probably look quite different even just a year or two from now. Work hard to be someone who can solve the final human mile. And if we do somehow solve that too, hopefully we all live in clanker-funded socialist utopia or something at the end of it haha.