This _was_ done a couple of decades ago, it was available on the downloadable version of google earth (when it existed). I remember playing around with it in 2012.
This is just false. For starters, your users most _definitely_ don't care about how "elegant" your code is either. They want new features to keep them engaged, or to make the product better. The quicker they get those wants fulfilled by your product, the less likely they are to churn off your product onto something else that has those features that they are missing.
The only people who care about code elegance are the people looking at the code, which is orders of magnitude fewer people than those who are _using_ the artifacts of what the code actually represents.
This is obviously the case to me, but I think HN is very anti-AI.
I genuinely don't believe that they sat down in a board room and said "yeah lets specifically release this now before an IPO so we can juice it!" They haven't even announced an IPO date. So is every blog on capabilities before that date just "pumping up the value of the stock before the IPO?"
Its AI. Both AI actually being good now, and CEOs believing AI is good. Even at most big tech companies, L3 engineers were basically the "claudes" of their team (or at least, 80% of their job was basically to be a "claude"). You give them some well scoped, largely coding task (that you either delegate as part of a larger project, or just something you don't want to do), and then they execute it. Once they build trust and learn the system, then they get larger projects, and then promotions.
Places are still hiring juniors, but now the bar for them is just much much higher, because a large portion of the junior level work (i.e, execution on implementation details), is pretty much 80-90% done by some senior + AI at this point, whether people believe it or not. This was certainly the case at the FAANG I was at.
I don't know why this "COVID overhiring" rhetoric is still a talking point. Covid was over 5 years ago at this point, those "overhires" have already left or have been flushed out from all these layoffs. Are you telling me the "COVID" overhiring resulted in such a huge surplus that, 5-6 years later, they are still on the market?
Conspiratorially, it seems like a shotgun attempt at undermining the supposed OpenAI IPO later this year.
Also filing an S-1 doesn't actually indicate that they intend to go public "immediately," it just gives them the option to go public (probably in the near future).
SpaceX is _not_ profitable by most reasonable measurements of accounting. If you discount rocket depreciation costs and R&D, then yeah its profitable from starlink revenue.
Apart from your last paragraph which is a little contentious, I agree with what you say.
I dont understand why people here require that every tech ceo to be some professional programmer or engineer. I don't think you _need_ to be that deep in it as the CEO. There are plenty of leaders at OpenAI that already fit the bill.
Sam is good at getting funding, seeing the bigger picture, and rallying towards a cause. That is the job of a CEO. It doesn't matter (imo) that he doesn't know how many parameters the next release will have. All that matters is he knows the impact of the new release and knows who to defer to for actual technical decisions.
The joke is that "taste" usually implies you have some strong personal sense of self and style, but if you walked into tech offices in the bay area everyone looks like that and acts/talks the same.
So its ironic that these same people are talking about "taste" when they ostensibly have very little.
The thing is, do humans _need_ most software? The less surfaces that need to interact with humans, the less you need humans in the loop to design those surfaces.
In a hypothetical world where maybe some AI agents or assistants do the vast majority of random tasks for you, does it matter how pleasing the doordash website looks to you? If anything, it should look "good" to an ai agent so that its easier to navigate. And maybe "looking good" just amounts to exposing some public API to do various things.
UIs are wrappers around APIs. Agents only need to use APIs.
I think "taste" is definitely an overused meme at this point, its like tech twitter discovered this word in 2024 and never stopped using it (same with "agency", "high leverage", etc).
Having read the article, I think I see the author's argument (*). I think "taste" here in an engineering context basically just comes down to an innate feeling of what engineering or product directions are right or wrong. I think this is different from the type of "taste" most people here are talking about, though I'm sure product "taste" specifically is somewhat correlated with your overall "taste." Engineering "taste" seems more correlated with experience building systems and/or strong intuitions about the fundamentals. I think this is a little different from the totally subjective, "vibes based taste" that you might think of in the context of design or art.
Now where I disagree is that
1. "taste" is a defensible moat
2. "taste" is "ai-proof" to some extent
"Taste" is only defensible to the extent that knowing what to do and cutting off the _right_ cruft is essential to moving faster. Moving faster and out executing is the real "moat" there. And obviously any cognitive task, including something as nebulous as "taste," can in theory be done by a sufficiently good AI. Clarity of thought when communicating with AI is, imo, not "taste."
Talking specifically about engineering - the article talks about product constraints and tradeoffs. I'd argue that these are actually _data_ problems, and once you solve those, tradeoffs and solving for constraints go from being a judgement call to being a "correct" solution. That is to say, if you provide more information to your AI about your business context, the less judgement _you_ as the implementer need to give. This thinking is in line with what other people here have already said (real moats are data, distribution, execution speed).
I think there's something a bit more interesting to say about the user empathy part, since it could be difficult for LLMs to truly put themselves in users shows when designing some interactive surfaces. But I'm sure that can be "solved" too, or at least, it can be done with far less human labor than it already takes.
In general though, tech people are some of the least tasteful people, so its always funny to see posts like this.
Well considering basically the entire market was down these past few days, Google included, its unlikely attributable to this paper alone. Its most likely correlated with general war/trade route restrictions/potential recession fears, or at least, more correlated with those than it is with this paper.
This paper was released a year ago and was probably part of how google got to 1m context before other labs.
Answer: Any job where the majority (or all) of your work can be done strictly by using a computer, and for tasks that have easily verifiable and objective outcomes. And from an economic perspective, jobs that have the highest cost (i.e, highest margins for AI companies to replace) have a strong economic incentive to be automated first. So Software, Finance, Accounting, Law, etc.
Yes - this means software engineers are likely the first to go, along with other high paying computer jobs.
yeah and 2s has not been doing too hot for a few years now. Jane street I buy - they tend to recruit a lot of CMU students. But definitely less than < 15 of the new grads they hire each year are from CMU. They maybe hire on the order of 50-100 new grad SWEs a year.
It will probably be a lot worse since white collar workers (especially the ones that AI is targeting, like banking, software, etc since they are super high margin jobs to automate) traditionally make and spend more than the average worker.
These are the people getting mortgages and sending kids to private school and whatnot. If their spending power suddenly drops to 0, its probably going to be pretty bad. I wonder what the housing market would look like in these cases.
I agree. I think most companies would be better off being 100% AI driven since synchronization problems for agents (or whatever the fad will be) is likely much lower than human social synchronization, and has more rich information transfer between "workers" (so less ambiguity, less tradeoffs to be made, etc).
As soon as a person enters the loop you add a manual sync point that probably doesn't need to be there. I think this is why you are increasingly seeing companies tell their people to be "on the loop" or "out of the loop" with their AI. The less syncing with a person, the better. And I think once this experiment runs its course, we will probably find out that human social interaction matters much less than we thought it did, especially for super transactional things like a corporate job where most of your work is done on a computer.
> ... Just that it doesn’t replace the social, human, and relationship based aspects of work, whether this is trust, or just being interested in what someone else says.
Yeah I also don't buy this. Most white collar work _seemingly_ necessitates trust, social/human aspects, etc. because we _have_ to interact with other humans, and the way we interact with each other is lossy and often has misaligned or not explicitly stated motivations.
In other words, most white collar work _seems_ bottlenecked on people-centric things because we have imperfect information about what other people want, so we have to use soft skills (i.e, skills only real humans have) to actually figure out motivations of various stakeholders and align expectations, garner favor, etc. amongst all of them. In a world where most of the workforce is AI, I think this problem of tacit information gets largely solved, since AIs can in theory, convey their intent and losslessly send information to one another without the need to waste time "aligning."
The other thing that people argue, especially in software, is that architecture and tradeoff decisions will remain in the human realm, because apparently only people have the "taste" to pick and chose the right solutions. I also think that:
(1) this will be easily solved by AI/current LLMs, since logically there shouldn't be a big difference between designing and writing good code to designing good systems architecture, and LLMs are ostensibly already good at coding
(2) "taste" and "tradeoffs" are things that, if you had more information (once again, if you could convey most or all necessary information losslessly between everyone in your org), things that appeared to be "tradeoffs" before might just be binary solutions.
Also just practically speaking, the stated goal of AI companies is to automate all labor. They won't just sit back happily collecting checks if there are parts of the human parts of the economy which they can't automate, that's revenue that they could easily capture. Whatever people claim AI lacks today will just be added to it in 6 months, AI companies are strongly incentivized to work towards this.
And at the end of the day, work is a transaction between employees and employers. A company's primary purpose is to generate money for shareholders, and human labor is just how it gets done. It doesn't matter if I _want_ to talk to a nice coworker instead of Claude 4.6 opus. If Claude costs less than my nice worker and has the same or better output, the company will happily replace that coworker with Claude because its strictly beneficial for the company.
1. ai being able to code well seems like it would also get pretty close/good at doing basically everything else you described. If coding is a game of reasoning, if you can solve that, you have effectively solved reasoning and you can likely map it to most other problems provided you have a sufficiently good harness and toolcalling setup.
2. Lets assume AI won't replace everyone as point (1) assumes - and it just replaces _most_ people. Under this assumption, we will likely see large swathes of layoffs. Many SaaS companies have a pay per seat model. Less people employed at companies = less seats being paid for = less SaaS revenue.
So not only is there a threat of companies just vibe coding various SaaS-es in house, but there is also a threat that the TAM of many SaaS products (which is typically proportional to the # of employees there are) will actually _shrink_ in size.
I think the main class of SaaS company that will remain in the medium term are the ones in legally touchy or compliance heavy industries - think healthcare, finance and security (workday for example). But even Workday will be affected by point (2) from above. Overall, I think the mid-long term outlook for SaaS, especially "SaaS", is not great.
I don't necessarily think you're wrong, and in general I do agree with you to an extent that this seems like self-centeted Computer Scientist/SWE hubris to think that automating programming is ~AGI.
HOWEVER there is a case to be made that software is an insanely powerful lever for many industries, especially AI. And if current AI gets good enough at software problems that it can improve its own infrastructure or even ideate new model architectures, then we would (in this hypothetical case), potentially reach an "intelligence explosion," which would (may) _actually_ yield a true, generalized intelligence.
So as a cynic, while I think the intermediary goal of many of these so-called-agi companies is just your usual SaaS automation slop because thats the easiest industry to disrupt and extract money from (and the people at these companies only really know how software works, as opposed to having knowledge of other things like chemistry, biology, etc), I also think that in theory, being a very fast and low cost programming agent is a bit more powerful than you think.