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RIP Peter G. Neumann

[Posted May 17, 2026 by corbet]

We have received word that Peter G. Neumann, who, among many other things, ran the RISKS Digest for decades, has passed away. He will be much missed.

Update: the New York Times has published an obituary of Dr. Neumann.

to post comments

Thoughts from a younger generation..

Posted May 18, 2026 12:18 UTC (Mon) by jpeisach (subscriber, #181966) [Link] (47 responses)

First of all, rest in piece. He will be missed.

Second, as time goes on, the greats of Computer Science will continue to pass away. This leads to the people of today - of which, who will be the successor?

I may be looking at things from the wrong perspective, but I worry about this and a general "loss of knowledge". When I think of "Computer Science" today, the first thing that comes to mind is everyone using web development frameworks, like React, which feel like all anyone can talk about. It feels that we are slowly drifting away from the original math, thinking, and *science* that makes up Computer Science. We're losing the people who launched this field, and I don't know the replacements, nor if they really are, the future of computer science. People who really know what they are doing. People who think and reason, instead of just doing whatever.

I don't know if I'm making sense, but does anyone else feel like this?

Thoughts from a younger generation..

Posted May 18, 2026 13:10 UTC (Mon) by dvrabel (guest, #9500) [Link]

Trying dividing people into computer scientists (maths/theoretical), software engineers (design/process), and programmers (craftspeople), then I suspect the absolute number of computer scientists continues to grow at a slow rate, whereas the number of programmers increases at a ludicrous rate, skewing their percentage share in their favour.

Thoughts from a younger generation..

Posted May 18, 2026 13:30 UTC (Mon) by paulj (subscriber, #341) [Link] (39 responses)

The "brains" of computer science today are spent optimising overwhelming-brute-force algorithms, which are pretty much impervious to any kind of mathematical analysis. So.. not much scope for maths there.

And generally, everything in computers now revolves around applying ever greater brute-force. Collectively, we appear to have decided to stop solving problems directly, but instead we apply said massive brute-force. We don't even code anymore, we get the energy-hungry, brute-force machines to do that.

Thoughts from a younger generation..

Posted May 18, 2026 14:45 UTC (Mon) by dskoll (subscriber, #1630) [Link] (38 responses)

We don't even code anymore, we get the energy-hungry, brute-force machines to do that.

This is going to be a huge problem. When people don't bother learning to write software, they eventually won't be able to check the output of LLM code generators, and then we'll be in real trouble.

Thoughts from a younger generation..

Posted May 18, 2026 15:26 UTC (Mon) by jpeisach (subscriber, #181966) [Link] (9 responses)

> When people don't bother learning to write software, they eventually won't be able to check the output of LLM code generators, and then we'll be in real trouble.

Well, the LLM code generators will probably be correct, it's just going to be code that is messy, slow, and miserable to work with.

Thoughts from a younger generation..

Posted May 18, 2026 15:53 UTC (Mon) by bertschingert (subscriber, #160729) [Link] (8 responses)

I feel like I have the opposite experience working with LLM generated code. The fact that it's perfectly formatted and follows conventions / best practices to a T means that it isn't always "messy... and miserable to work with". I've actually found it to be easier to read than code written by a junior developer.

The issue is that it definitely isn't "probably... correct". Hallucinations are still very much an issue. Just the other day I reviewed an LLM written codebase that implemented a specified protocol. There was a point in the code that blatantly violated the spec... right beneath a comment that said

> /* Follow RFC xxxx Section yyyy */

and then it did something that was trivially and obviously wrong with respect to that section of the RFC.

So my experience is that LLM written code is actually quite readable and nice to work with. It just might happen to be completely wrong.

Thoughts from a younger generation..

Posted May 18, 2026 17:05 UTC (Mon) by mb (subscriber, #50428) [Link] (7 responses)

Yes, LLMs make mistakes.

But things like that are typically a result of not doing a proper development cycle.
Code generation (code writing) is not a full cycle.

Proper development, both human and AI, require things like:
- Requirements review
- Design review
- Code review
- Failure mode analysis
- Development of coding guidelines
- Coding
- etc...

If you just use an LLM to do the coding step and then say you are done, you leave out the most important steps.

Things like /* Follow RFC xxxx Section yyyy */ and the code doing something different are actually not that hard to find with AI tooling.

If a proper development cycle is used, then AI generated code can be quite good. Much better than hand-coded code from most developers.

Thoughts from a younger generation..

Posted May 18, 2026 23:14 UTC (Mon) by Baylink (guest, #755) [Link] (6 responses)

" if a programmer can simulate a construct faster than the compiler can compile it, then the compiler writer has blown it badly."

That quote is from one of the top 10 authors in the field, Aho, Sethi, Ullman, Kernighan, Pike, Plaugher... one of them. I feel like it was Rob but I can't remember for sure. But the concept applies here too: if you have to back check the LLM, it doesn't qualify.

Thoughts from a younger generation..

Posted May 19, 2026 1:37 UTC (Tue) by rsidd (subscriber, #2582) [Link] (4 responses)

Question is, is it still faster than a human. That is,
* human writes the code
* 2 layers of peer review

vs
* LLM writes the code
* AI peer reviews it
* Human does second peer review

In many cases the second is already a win, in both speed and reliability. The gap will increase. But LLMs can implement well-known algorithms quickly; I don't think they can reliably construct new algorithms. Yet. That's where theoretical computer scientists still have a role.

Thoughts from a younger generation..

Posted May 19, 2026 11:00 UTC (Tue) by nix (subscriber, #2304) [Link] (3 responses)

I don't know. Peer review of human code is difficult, but good peer review of AI-written code is in my experience nearly impossible because, by construction, it always looks plausible, while often being wrong nonetheless. The result is demonically difficult to review properly, because none of the usual tells that code is wrong exist (a major tell for me that something needs closer inspection is code that somehow "feels uncertain", like it was rewritten multiple times while trying to get the bugs out of it: none of that with AI at all). It turns out most of those tells are testing for properties of *humans* writing code, not properties of the code itself.

It's like that thing with chess grandmasters -- if you hit them with normal boards they can manipulate them mentally with amazing speed, but if you hit them with random boards they're no better than merely good chess players, and make as many mistakes as well. So much of experience is grouping knowledge and memory into ever-larger chunks that can be handled by non-conscious mental processing; with AI-written code, much of that chunking is valueless because these are not human beings and (even when mimicking humans) they do things that humans do not do, throwing off the chunking and leaving you either scrambling in the dark or misled and missing errors in the flood of outwardly-plausible-looking code.

I find reviewing AI-written code so unpleasant and draining that honestly simply throwing it away and rewriting it by hand gets more tempting every time I do it. (Then having AI models review that, because they are very good at finding mistakes -- mistakes a human would also find if they had superhuman concentration, but of course nobody does.)

Thoughts from a younger generation..

Posted May 19, 2026 11:02 UTC (Tue) by nix (subscriber, #2304) [Link]

Oh, and the "have the AI write code, have another AI review it, then review it yourself" works less than you might hope -- in most cases I've tried, the AIs overlook errors that humans are not likely to have made, such as... all the inhuman hard-to-spot errors made by the AIs writing the code in the first place.

Sometimes you're lucky, but you have to wade through so much horrible stuff to get there...

Thoughts from a younger generation..

Posted May 19, 2026 11:32 UTC (Tue) by pizza (subscriber, #46) [Link]

> I find reviewing AI-written code so unpleasant and draining that honestly simply throwing it away and rewriting it by hand gets more tempting every time I do it. (Then having AI models review that, because they are very good at finding mistakes -- mistakes a human would also find if they had superhuman concentration, but of course nobody does.)

In my professional [1] and f/oss [2] niches, it's not just "unpleasant and draining" but usually *slower* to review versus rewriting from scratch. As an added benefit, said rewrite is instrumental in building/sustaining the mental models / understanding necessary to perform meaningful debugging and medium-to-long-term maintenance.

I'll add that this applies 3rd party code in general, but LLM-produced stuff is particularly bad IME. Sure, it *looks* amazing and is "explained" confidently, but you have to go over it line by line, checking literally every detail for accuracy.

[1] building models of various SoCs and peripherals for virtualization
[2] device drivers and other baremetal stuff

Thoughts from a younger generation..

Posted May 20, 2026 9:10 UTC (Wed) by neggles (subscriber, #153254) [Link]

Write tests first. Helps a lot. Even just having the bot write tests alongside the code often catches things, and it's often significantly easier to spot a bad test than a bad impl.

Origin of the quote.

Posted May 19, 2026 2:16 UTC (Tue) by devnull13 (subscriber, #18626) [Link]

This quote was coined by computer scientist Guy L. Steele Jr.. It was popularized after being featured as a "bumper sticker" in Jon Bentley's famous 1985 programming column and book, Programming Pearls.

Thoughts from a younger generation..

Posted May 18, 2026 15:58 UTC (Mon) by paulj (subscriber, #341) [Link] (27 responses)

The LLM operators will have to become experts in carefully specifying the functionality they want the LLM to implement. Perhaps ultimately we will end up formulating technical specification languages, to help humans precisely communicate the requirements to the LLMs. And then the humans can get LLMs to help them prepare the specifications in such technical specification languages, for other LLMs to implement... And...

Basically, "programming" is going to be like the requirements gathering phase of software development today. The phase where, *today*, someone meets with {internal,external} clients / "stakeholders" and brainstorms or walks through exactly what they want from the product, goes away and puts that in documents (technical documents, storyboards, mock-ups, etc.), then goes back and meets them again to refine, and so on - to ultimately produce something to give to the developers to implement. Who will read the documents and point out all the problems (inconsistencies, impossibilities, etc.), and the person has to go back to the clients/stakeholders and refine further. Etc., etc.

That person is sometimes called a product manager. And that portion of software development is universally considered the _really fun_ part of the job.

And in the marvelous future ahead of us, we'll get to do all this, except with LLMs, and we can get rid of that horribly boring bit - the actual software development! Yay! And it'll be all be so much more efficient!

Thoughts from a younger generation..

Posted May 18, 2026 16:05 UTC (Mon) by paulj (subscriber, #341) [Link]

Oh, and we won't have to worry about verifying that the LLMs actually produced code that matched the spec, cause as we all know, LLMs are incredibly good at following specifications and prompts /exactly/, and never invent stuff.

The future looks so bright!

Thoughts from a younger generation..

Posted May 18, 2026 16:17 UTC (Mon) by jpeisach (subscriber, #181966) [Link] (21 responses)

> And in the marvelous future ahead of us, we'll get to do all this, except with LLMs, and we can get rid of that horribly boring bit - the actual software development! Yay! And it'll be all be so much more efficient!

And I'm sure it will be worth the time, effort, electricity, water, and energy to do all the training and LLM directing instead of just manually coding it.

Thoughts from a younger generation..

Posted May 18, 2026 21:11 UTC (Mon) by kleptog (subscriber, #1183) [Link] (20 responses)

Well, there's no particular reason why training has to be so expensive. It's just we haven't yet figured out how to do it better. But we will, I'm sure of it.

Reminds me of the story of a telephone exchange being built for a new suburb, three storeys high. By the time they had actually completed construction the hardware required fit in the basement.

Currently there are more GPUs planned to be installed than we have power to switch on. So there is a huge amount of pressure to make the process much more efficient.

Thoughts from a younger generation..

Posted May 18, 2026 21:19 UTC (Mon) by dskoll (subscriber, #1630) [Link] (19 responses)

Pretty much all the big AI companies are bleeding cash, and bleeding it really, really fast.

You can bet that once people are absolutely dependent on AI, the price hikes will be breathtaking. Possibly to the point where human software engineers might start looking cost-effective again, just like in The Feeling of Power.

Thoughts from a younger generation..

Posted May 18, 2026 22:14 UTC (Mon) by malmedal (subscriber, #56172) [Link] (18 responses)

This "bleeding cash to serve the LLMs" is pure fiction. They are spending a lot of money to build datacenters, probably too much, but even if they can't handle their loans the serving is profitable enough that the top providers will just go to chapter 11 and continue to operate.

I do expect the prices to go up soon, though. Simply because the demand is so much higher than the supply. Arguably this has already happened. Anthropic's latest model Opus 4.7 uses 40% more tokens on the same input as 4.6. The headline token price is the same, but you'll be paying more.

Thoughts from a younger generation..

Posted May 19, 2026 8:40 UTC (Tue) by paulj (subscriber, #341) [Link] (17 responses)

The top software-tech companies are venturing into modes of business operation they've never been in before. They have _never_ had such vast CapEx programmes before. And they do _not_ have the revenue in hand today to make that CapEx profitable. They have an /expectation/ of future increased revenue.

Some of the big tech companies are funding this huge CapEx increase with profits from their existing, hugely profitable business - i.e., advertising for the likes of Facebook. So, should that /expectation/ of future significantly increased AI revenue not quite arrive, they should be able to cope. And they have that other revenue to allow them to fund R&D/engineering to find other uses for that CapEx if needs be. The ones who funded that CapEx with debt, who did /not/ have another vast revenue source to fund this CapEx with, well they... or else their lenders, may take quite a hit.

Course, AI is the bright future, and it's incredible ability to pour out unending reams of highly accurate information (documents, code, etc.) *of course* is going to result in *huge revenues*. It's obvious! Look at all the white-collar jobs companies will be able to pull and replace with AI. It's going to save *loads* of money! And as everyone knows, cutting swathes of jobs in an economy is *guaranteed* to result in huge economic growth - the AI companies are going to reap the bounties of those savings, oh yes!

Thoughts from a younger generation..

Posted May 19, 2026 10:27 UTC (Tue) by malmedal (subscriber, #56172) [Link] (16 responses)

Almost every new technology has gone through these overinvestment boom and bust cycles (only exception I can think of is that cars don't exactly fit the standard pattern).

It will be very weird if the current AI boom does not get a corresponding bust in the near future. But like computers, the telegraph, railroads etc. it's clearly useful and is going to persist after the bust.

Thoughts from a younger generation..

Posted May 19, 2026 11:13 UTC (Tue) by paulj (subscriber, #341) [Link] (15 responses)

I agree with you generally. With the caveat that the current "AI" over-investment cycle is one of the more extreme cases. E.g., the "AI" investment hype-train may be the only thing that has kept the US economy (and hence rest of the western economies) out of recession the last year or two.

"AI" is not really new technology. What is new is the level of brute-force compute we can throw at it today. It is certainly useful for some stuff - ability to query a vast data-set and extract answers from that data-set tailored to the query context (i.e., prompt), with a number of features from the data-set stitched together in somewhat meaningful ways according to the prompt, is indeed very useful. It also has problems - in particular, the technology doesn't understand what it is doing, and is generally trained to be authoritative and massively over-confident (i.e. "helpful"). It is frustrating the level of faith people will put into the output, in part cause of that trained-in over-confident tone in every AI (that I can find).

I think the hype will subside. Utility will certainly remain, but it's nowhere as high as the industry thinks - particularly in the ultimate metric of sustainable revenue, I greatly suspect. That of itself will lead to further issues for the AI industry - as revenue fails to meet the required levels to pay off the investment and OpEx (energy isn't getting cheaper), they will need to squeeze more from each user, which will deter some users, etc., and so the sustainable revenue may be a lot lower than people think.

We shall see...

But, the gigantic profits simply can not be there. A technology whose success - i.e. massive, gigantic profits - is predicated on making large chunks of the white-collar classes of western economies redundant - is a technology that has to somehow thrive in the recession it must create in order to thrive. A paradox.

Thoughts from a younger generation..

Posted May 19, 2026 13:31 UTC (Tue) by Wol (subscriber, #4433) [Link] (12 responses)

> That of itself will lead to further issues for the AI industry - as revenue fails to meet the required levels to pay off the investment and OpEx (energy isn't getting cheaper), they will need to squeeze more from each user, which will deter some users, etc., and so the sustainable revenue may be a lot lower than people think.

The other big problem that could bite, is that as they try and squeeze more revenue from the customers that bought into the hype, those customers could find themselves under attack from companies that didn't (or startups), and then the AI companies could just drag their customers down with them if the customers can't disengage fast enough.

Cheers,
Wol

Thoughts from a younger generation..

Posted May 19, 2026 14:52 UTC (Tue) by dskoll (subscriber, #1630) [Link] (11 responses)

All we need to do is look at what happened after Broadcom acquired VMWare to understand the pricing strategy likely to be used by AI companies once customers are hooked.

Thoughts from a younger generation..

Posted May 19, 2026 15:25 UTC (Tue) by malmedal (subscriber, #56172) [Link] (10 responses)

Switching from one virtualization platform to another is one of those things that sounds like it should be easy, but isn't. Especially VMWare.

Switching from one LLM to another on the other hand is trivially easy. The providers are working very hard to find some kind of lock-in, but so far without success. Also some of the attempts that are closest to working are problematic, e.g. Claude for Excel depends on Microsoft cooperating.

Thoughts from a younger generation..

Posted May 19, 2026 16:11 UTC (Tue) by dskoll (subscriber, #1630) [Link] (4 responses)

Switching from one LLM to another on the other hand is trivially easy

Perhaps today (though even that, I'm not sure of), but I expect there will be consolidation in the LLM space and we'll be left with 3-4 giants who will all adopt the Broadcom strategy. It wouldn't make sense not to.

Thoughts from a younger generation..

Posted May 19, 2026 16:55 UTC (Tue) by malmedal (subscriber, #56172) [Link]

As I said, they are trying hard. But so far without success.

There's still an escape valve that can't be taken away, We currently have free models that works on modest hardware while being comparable in quality to top commercial models from about 12 months ago. (Qwen 3.6 27B and Gemma 4 31B). And less than six months if you can afford immodest hardware.

Thoughts from a younger generation..

Posted May 19, 2026 19:12 UTC (Tue) by Cyberax (✭ supporter ✭, #52523) [Link] (2 responses)

As an example of local models, I have two AMD graphics cards with 64Gb total RAM and Qwen 3.6 was able to autonomously write an implementation of LTFS (a filesystem for tape drives) in Go, given the spec from SNIA and the reference implementation in C. In about 7 days.

I just wrote a detailed plan and left the agent to its own devices inside a sandbox. It first used a flat file backend to validate itself against the C implementation and then fuzz its own code. It found several security bugs in the C implementation, huge surprise. It's now working on adding real device support via iSCSI to a real tape drive.

All of this on hardware that is available for casual hobby users, no unobtanium-level hardware. Even _this_ level of competency from models is enough to seriously affect the way software development works.

And free models are still getting better. Qwen 3.6 is just 35B parameters, this is the level where you can feasibly _train_ a model from scratch independently without being a hyperscaler. It'll still cost you hundreds of thousands, but it's not out of reach for a non-profit or a crowd-funded startup.

Which cards?

Posted May 19, 2026 22:10 UTC (Tue) by edgewood (subscriber, #1123) [Link] (1 responses)

Would you mind sharing which AMD cards?

Which cards?

Posted May 19, 2026 22:44 UTC (Tue) by Cyberax (✭ supporter ✭, #52523) [Link]

Sure, I have a pair of AMD Radeon AI PRO R9700. They were retailing for $1100 when I bought them last year but went up a bit after the RAMPocalypse. I like them because they don't require any special support and "just work" in the most recent Fedora.

I can see that the agents were using 300W on average for the whole machine (a mid-level Xeon-based server with 128Gb RAM, that I was lucky to get 2 years ago). I probably can do the same at a fraction of the time with OpenAI/Anthropic models, but that's just boring.

Thoughts from a younger generation..

Posted May 20, 2026 6:13 UTC (Wed) by anselm (subscriber, #2796) [Link] (4 responses)

Switching from one LLM to another on the other hand is trivially easy.

Perhaps, but that won't really help you because the LLM operators will all need to raise their prices dramatically if they want to have any chance at keeping their heads above water.

Thoughts from a younger generation..

Posted May 20, 2026 8:47 UTC (Wed) by farnz (subscriber, #17727) [Link] (3 responses)

However, that only applies if you require a frontier model that's only available via a remote API. There are free LLMs like Qwen 3.6 that get you a significant fraction of the abilities of the latest and greatest LLM operator models, and that can be run extremely well on local hardware costing under $10,000, plus whatever your energy cost is for a sub 1 kW tower PC (not a laptop - laptops tend not to have fast enough hardware for this sort of thing).

That puts a hard bound on how much the LLM operators can charge - their maximum price is bound by how much better than models like Qwen, DeepSeek and Gemma they are, and the cost of running those models locally. If you charge enough, people will just switch to local hardware, and local models over time - and you can't make up for that by charging more.

So the question ends up being "do the API-linked models provide enough value that it's worth paying whatever the LLM operators charge for them, or do you get better value from a local model?". And that's assuming that you get yourself tied into needing an LLM to begin with, of course.

Thoughts from a younger generation..

Posted May 20, 2026 11:34 UTC (Wed) by malmedal (subscriber, #56172) [Link] (2 responses)

Rumours are that Anthropic's margins on serving the LLMs are like 70% including what they lose by the free quota. I think this is plausible when you compare prices with the open weights models.

You can check openrouter.ai/models or hugginface.co/inference/ for pricing details.

Cost of LLMs in the cloud

Posted May 20, 2026 14:18 UTC (Wed) by farnz (subscriber, #17727) [Link] (1 responses)

That sounds like a plausible gross margin - the cost of running inference for you given that you have the datacenter, the hardware and the model already is relatively low. The expensive bit is developing new models, and building new datacenters.

And that's why the LLM providers can't raise their prices that far - if they do, then instead of paying extra or doing without LLMs completely, people will move to open models. The only way to avoid that is to charge fairly, or to have a model that's sufficiently better than any open model that it's worth paying your prices.

Cost of LLMs in the cloud

Posted May 20, 2026 14:34 UTC (Wed) by malmedal (subscriber, #56172) [Link]

Yes, I agree. I am arguing against what was said earlier in the thread about the models going away permanently because they are too expensive too run.

Thoughts from a younger generation..

Posted May 19, 2026 14:59 UTC (Tue) by malmedal (subscriber, #56172) [Link]

> The gigantic profits simply can not be there.

They *can*, I am sure the eventual business spend will be gigantic. However I don't know if profits *will* be gigantic. I can see it going multiple ways:

1) One actor gets so much better than the others that it captures almost all the money.

2) Multiple actors which roughly equivalent capabilities compete on price and are forced to sell at cost plus a few percent.
(most likely IMO)

3) Businesses buy hardware to run LLMs by themselves. Least likely, I think.

Hype isn't going anywhere at this rate

Posted May 19, 2026 19:37 UTC (Tue) by jpeisach (subscriber, #181966) [Link]

> I think the hype will subside. Utility will certainly remain, but it's nowhere as high as the industry thinks - particularly in the ultimate metric of sustainable revenue, I greatly suspect.

When employers are pushing developers to "use AI or else we will fire you" and awarding people for more AI use, then this isn't going to happen. I think the best bet for the hype to die down is:

a) data centers are built and start screwing people over
b) the environmental impacts become clear
c) these AI companies start charging people appropriately for its use

As for local LLMs: They tend to be slow, my guess is because of a lack of training? I don't know, I stopped trying after a while because I didn't want to waste my time and spend my laptop battery figuring it out.

Thoughts from a younger generation..

Posted May 18, 2026 19:18 UTC (Mon) by Wol (subscriber, #4433) [Link] (3 responses)

> Perhaps ultimately we will end up formulating technical specification languages, to help humans precisely communicate the requirements to the LLMs. And then the humans can get LLMs to help them prepare the specifications in such technical specification languages, for other LLMs to implement... And...

Don't we have things like that already? It's just that currently they're called compilers, not LLMs ...

Cheers,
Wol

Thoughts from a younger generation..

Posted May 18, 2026 20:29 UTC (Mon) by malmedal (subscriber, #56172) [Link] (2 responses)

What's actually been happening lately is that the LLMs have been instructed to ask the humans clarifying questions when instructions are ambiguous.

Thoughts from a younger generation..

Posted May 19, 2026 8:31 UTC (Tue) by paulj (subscriber, #341) [Link] (1 responses)

Ah, ok. Well, that's completely different from current compilers highlighting ambiguous/undefined/error-containing code with a warning/error; and totally different from programmers today asking clarifying questions on a spec from a customer (directly or indirectly via a PM). ;)

The future is so bright. At *last* we have our magic bullet for software engineering!

Eat *that* Brooks!

Thoughts from a younger generation..

Posted May 19, 2026 10:15 UTC (Tue) by malmedal (subscriber, #56172) [Link]

It's very different in that you're operating at a much higher level of abstraction. By several orders of magnitude compared to previous attempts at report generators and wizards.

It's also being done by non-programmers, like the defense editor at The Economist wrote that he had made a program to help him keep track of aircraft movements in the middle east.

Thoughts from a younger generation..

Posted May 18, 2026 23:11 UTC (Mon) by Baylink (guest, #755) [Link]

+3.141592653589646

Thoughts from a younger generation..

Posted May 19, 2026 7:05 UTC (Tue) by autious (guest, #114303) [Link] (4 responses)

I've worried about this myself, going through a college masters degree and hearing classmates complain about learning the foundations and underlying theory, wanting to prioritize popular tools and "what will get me a job asap". We had a course in compilers and scripting held by a professor who was developing runtimes and compilers for Simula during a time where it was a true contender vs C++, and I feel most people didn't do enough to truly appreciate it.

There are those who do truly love the field though. I'm hopeful there aren't too many forces killing that interest. The noise of the software industry being seen as easy money will drown it out until that era passes.

Thoughts from a younger generation..

Posted May 19, 2026 9:28 UTC (Tue) by farnz (subscriber, #17727) [Link] (3 responses)

I went through university over 2 decades ago, and remember people complaining that we were learning parsing theory, graph theory, formal methods, boolean logic and other such things, when we should have been learning how to write Java applets for the web, because that was clearly the future of work. Quite a few of the loudest complainers didn't make it to their second year of undergraduate in CS, because it wasn't what they were interested in; and of the remainder, none are still in the "high paid" jobs they wanted to get via a CS degree, but have gone into lower paid roles connected to the field (e.g. project management of software projects, marketing/sales for tech firms etc).

I suspect that the same is true today - you've got the people who are interested in CS for CS's sake, and a much larger group who just want a comfortable high-paid job - and the hard question is whether the first group is growing or shrinking over time (since it's covered by the noise created by the larger group who just want a job out of it).

If you only want to work, why live?

Posted May 19, 2026 11:27 UTC (Tue) by jpeisach (subscriber, #181966) [Link] (1 responses)

If the goal of a CS degree is only "just get me a job", then you aren't contributing to the field in any meaningful way. You're just.. applying it. Which, in itself, can be cool, and I think where we see the "noise" of the "larger group of people" is that the applications are indeed, meaningful.

I'm going to begin university in August, but I do go to a school where I had a brief introduction to Complexity Theory, Computational Theory, and I also took an intro to Graph Theory course. I think they *all* help you understand computers better. It's honestly kind of magical, seeing how these concepts create what we use today.

If you only want to work, why live?

Posted May 19, 2026 12:35 UTC (Tue) by iabervon (subscriber, #722) [Link]

It's similar to how construction workers aren't contributing to the fields of architecture or structural engineering in any meaningful way (beyond causing actual buildings to actually exist, but only ones that other people had already determined were structurally sound and possible to build). And, of course, they're contributing to humanity, which needs these buildings, and they're making the study of architecture and structural engineering useful for a practical purpose.

Similarly, just being able to write software competently allows you to contribute to the world benefiting from the use of computers, but it's not really the purpose of a CS degree. About 25 years ago, my CS department started teaching a programming class for non-majors, because, these days, biologists are going to write programs to apply their knowledge of biology, and you'd like them to write those programs well, and the CS department figures out what "writing programs well" means.

Thoughts from a younger generation..

Posted May 19, 2026 12:56 UTC (Tue) by gioele (subscriber, #61675) [Link]

> I suspect that the same is true today - you've got the people who are interested in CS for CS's sake, and a much larger group who just want a comfortable high-paid job - and the hard question is whether the first group is growing or shrinking over time (since it's covered by the noise created by the larger group who just want a job out of it).

I like how the German tertiary education system solves it (or used to solve it, nowadays the demarcation is very fuzzy): Universities focus on producing new scientists and Fachhochschulen (better translated as "university of applied sciences" rather than "vocational colleges") produce workers, but with an above-industry-average knowledge of theoretical subjects.

Those who like CS for the sake of it can study at a University (and maybe get a career as researchers or professors). Those who want an high-paid job are better suited by a Fachhochschule (where the teachers are hi-profile practitioners from the industry and will help them land a good job).

Another of the great old ones passes

Posted May 18, 2026 13:19 UTC (Mon) by nix (subscriber, #2304) [Link]

Oh no :( :( :( one of the old immortal greats, and perhaps one of the best list moderators ever. But it turns out that nobody is immortal after all.

RISKS Archive

Posted May 18, 2026 23:10 UTC (Mon) by Baylink (guest, #755) [Link] (2 responses)

Lindsey Marshall has run a web archive of the RISKS digest postings for some years, at Newcastle:

https://catless.ncl.ac.uk/Risks/

As I recall, he, too, is getting up there and hopefully -- there's lots of good material in those archives, plus a couple of postings from me :-) -- the Internet History Foundation can take care of perpetualizing that material somehow.

It is not clear whether there was an heir apparent to run the feed or not, but I feel like if there is one, it's better than 50/50 they are either reading this, or they know somebody who's reading this.

I know Brewster Kahle is pretty busy, but maybe somebody knows him and can drop this across his transom?

RISKS Archive

Posted May 19, 2026 3:54 UTC (Tue) by pabs (subscriber, #43278) [Link] (1 responses)

ArchiveTeam saved that domain to archive.org some years ago:

https://archive.fart.website/archivebot/viewer/?q=catless...
https://wiki.archiveteam.org/index.php/Main_Page

RISKS Archive

Posted May 19, 2026 3:56 UTC (Tue) by pabs (subscriber, #43278) [Link]

I just started another job to capture the updates to the site since 2023.

http://archivebot.com/?initialFilter=catless.ncl.ac.uk


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