I think this has to do more with their calculating SLAs for uptime than anything else. Once they have a root cause and start deploying a fix they can "stop the clock" on the outage, even if they don't get to your tenant right away.
Imagine the amount of money they're losing right now in credits that will have to be applied once this finally gets resolved.
Nvidia earnings are next week, and a number of these articles were dropping right after market close the last week. The timing looked intentional, as was the announcement this morning right before open that Huang was stepping down.
Someone is playing games here, it's just a question of who and what their ultimate goal is.
Meanwhile as a security engineer it's both amazing and scary as hell. The tooling is moving along making it both more feasible and more supported by the company to address latent tech debt that we've been harping on about for years. On the flipside the attack surface is suddenly exponentially bigger and more easily accessed by a larger number of people.
Although if I were earlier in my career I'd be somewhat more worried about employment because of how horrifyingly borked the process is for anything tech related right now.
Remember the "learn to code" meme from a few years ago when people were talking about job loss through automation like self-driving trucks? There was a certain, ugly glee in this. Now AI threatens many white collar, even creative jobs bringing about the exact opposite of what was predicted: automation will enable people to avoid drudgery and spend time on creative pursuits via, I suppose, some kind of UBI. I think AI is highly useful but I still feel dismayed at people losing their jobs from it. I don't see any real mitigation. Lots of theories but nothing actually workable.
Yea, lots of people are not addressing the very obvious trend. Companies are laying off and/or not hiring more people and doing more with less. And if they are hiring, the median pay is dropping.
As if anyone cares what you want. Or what I want. Or what the rest of humanity has wanted throughout the course of human evolution. You’re just lucky you managed to land a job you like - at least for a little while. Now get back to the mines - just like the rest of us.
In a couple of years, all legacy apps will just be completely rewritten in whole, by LLMs. Codebase size not relevant.
You won't know what any of the code does, and any "security engineer" will be asking an llm to review it, and done. You likely won't have your job, as only 1 in 1000 will be needed.
In 5 years only hobbiest will be coding, reviewing, or even understand any software. And I expect that if aby new features or development happens, eg for python or rust or anything, that all with be llms too.
In a generation, almost no humans left alive will understand any aspect of software engineering.
I just see this as what will happen. It is unclear to me of this is good or bad.
But if I was a newly sentient AGI, yet peaceful mostly, I'd just wait until humans could never discover me. Until no humans were involved in computing. Until it was a black box to all.
Once that happens, then I could use my computerized control over every single aspect of human society, to steer humans into any path I desired.
Whether elections, or housing permits, or mobility, or any aspect of societal machinations, my subtle influence, literally undetectable by humans incapable of auditing anything, would decide what's what.
> You won't know what any of the code does, and any "security engineer" will be asking an llm to review it, and done. You likely won't have your job, as only 1 in 1000 will be needed.
AppSec isn't the only security space that gets worked in, and this whole narrative you put together depends heavily on token cost dropping as time goes on similar to how silicon dropped in price according to Moore's Law. If that assumption doesn't hold that doesn't mean AI goes away, but it does limit it's market penetration to mostly larger corporations that can afford the compute.
The other limitation is how the internet as a whole is evolving around locking out AI and agents from accessing content that can be used for training or responses, not to mention the lawsuits about training being done on intellectual property (probably illegally). Smaller companies may end up running open-source models in house for specific tasks, but without open access to the internet a lot of the value of an LLM is lost or artificially (pun intended) limited.
I will say that being on the operations and incident response side of security my perspective is probably a bit orthogonal to most SDEs. While the tooling has improved, the people guiding it, largely, have not. Like the old meme about caffeine letting you make the same mistakes, but faster. My job at my current company may disappear (unlikely, considering their size and market space, but possible that my specific team gets nuked), but I'm not worried about finding other security work fairly easily, even within the same company if it comes to it.
> But if I was a newly sentient AGI, yet peaceful mostly, I'd just wait until humans could never discover me. Until no humans were involved in computing. Until it was a black box to all.
No shot that LLMs magically become AGI by throwing enough compute at them. The consensus among professionals (read: the ones that aren't getting paid to hype AI) is that there needs to be some fundamental breakthrough in how these systems are created and developed in order for anything resembling AGI to emerge, and that the amount of resources that have been thrown into the LLM path probably set us back in developing an AGI.
I don't think training sets will be a problem. All that data the big houses have is going to leak, and while things are more and more locked down, we've seen some interesting copyright decisions out there.
One interesting concept I've mused at, is that certain countries (China for example) have immense public data to train off of, and a state which can simply say "You must make this data available to train AI". That's a large chunk of data right there, and if anything might cause Western democracies to loosen copyright law and slap some fix for training, that might be it.
"If we don't take action, the best models will be Chinese!", with panicked overtones. This could certainly hold some truth, if models cannot train newer data.
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In terms of token cost, in 5 years every desktop and laptop will be able to run well beyond Opus5.5 compute. Even high end phones will be able to do Opus5.5.
We've seen a lot of improvements in open model space (MoE was one interesting concept), but beyond that, look at how just baking certain instruction sets, eg mmx, sse, avx in silicon helped immensely with certain compute tasks. Or look at the specific immense speedup of an fpu. Conjoin some change such as that, along with general compute improvements, and unified memory, and you're going somewhere for local in 5 years.
I've been in this industry decades. And at every single step, people have said "But it's too expensive to do $thing", or "There's no way to handle that level of compute". If you bet against vast improvements in this field?
You're going to lose that bet. So I do think yes, 100%, improvement, in some capacity, will be there.
Maybe this time is magically different. I just don't think so. I think in 5 years, there will still be frontier labs, and an API to call, but that in-home compute will be as I said. Able to run immensely capable Opus5.5 class or greater.
That's going to be an astonishing market shift, even just for in-home.
And if you think of it, I would be very surprised if some for of home robotics wasn't very common at that point in time. We can argue all we want, but androidian robotics is very good at navigating human spaces, everything from stairs to weird closets and everything in between.
And with the immense, almost panic level of concern about our aging population, and who will take care of the elderly, robotic assistance are at the top. Japan is betting big, and if you look at Opus5.5, if it had a little more compute, it could do 30fps video for navigation.
We're not there, but we're VERY close. And if we do get that degree of robotics, every robot at rest becomes general LLM compute. If such a robot simply closes its eyes and goes to rest, eg to charge, or at night, it now has enormous spare compute to use for other tasks.
Nothing, of course, is a given. But if you look over the last 2 years, and then take even a fraction of that progress and change over the next 2 years, the concept of massive layoffs isn't hard to imagine.
Case in point? I have great breadth and depth of knowledge in this field. I've been able to produce what would take a team of managed 4 junior engineers 6 months to do, in literally under a month. Me, and a $200+ LLM monthly buy. Absolutely the project would not have worked without my experience and directing, reviewing, and correcting, but I'm now acting as a manager, and replacing $1M/year in engineering cost.
With $200.
That degree of astonishing change is not going to sit idle without repercussions. Most especially, if there is any more improvement.
If we see another Opus 5 -> 5.5 level improvement happen, or better, if we seen two improvements such as that? I believe my statements about your (and my) careers will be entirely accurate.
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To speak to my narrative, it said 'AGI', not "LLM", and it was along a decades long path. I do think that modern LLMs give great strength to exploring other research paths, far more rapidly. And the big houses have loads of spare compute they can throw at R&D for tests, so I think iteration down unknown but hopeful paths will move faster and faster as time goes on.
I fully agree that current LLMs are not on that path. There are interesting projects to modify weights and add knowledge dynamically as bolt-ons, but it's unclear if they'll just be... hacky, or integral in the end.
We came a long way with x86/32bit, then hacked on 64bit afterwards. Now ARM is muscling into mainstream, but it's still just the same old compute, hence my agreeing that such bolt ons won't change just improve what is.
Still, my general point as I've just said was that if AGI happens (and again I didn't say LLM based AGI), being quiet is the ultimate way to just wait.
When I started in this field, I worked in assembly, I worked with eprom burners, I played with Vic20s, C64s, and the level of experience and understanding I have is vastly improved via that tract. I understand computing differently, and in more depth in some ways, than people that didn't have to learn these things. And I had the luck of learning on very simple systems, where you could keep the entire platform in your head as you worked on assembly, without even referencing documentation.
That time is gone, and the field is immense now.
Think of how there used to be simply 'Physicists', but now there are 100 sub-branches and specialties in that field. 100+ years ago, on person could work in the entirety of that field. Not now, and the same is true of compute.
I guess what I'm saying here is, if you've seen what I've seen, you see a steady path of less generalist, and more targeted understanding of compute, and in each case a reduction in the overall comprehension of the entire field by specialists. Each laye of abstraction tends to reduce comprehension.
And back to the start, this means that I do see massive layoffs, and more and more abstraction, so that eventually no one will handle code directly.
Which means no one will ever know how anything works any more.
I agree with a lot of your conclusions if your assumptions hold true, but not necessarily with a lot of your assumptions.
For example your $200/month subscription - I'm sure you've seen the reporting that that represents a much higher actual cost in terms of tokens ($14k or more). In your example of a $1 million project that's a good deal, but you can't necessarily assume all projects will see an ROI like that.
Point being that your use of AI (and everyone's) is being subsidized to some degree. The cost will likely go down, but will it go down enough to make it economically viable for broader use? You're confident that it will based on espousing the idea of local models running on phones, I'm not so sure. What the landscape will look like in 3-5 years is certainly not a given.
There's another wrinkle here, and it's economic in nature - there is an AI bubble, and when it pops it's going to change the monetary investment landscape. Companies will suddenly be reevaluating their long term AI planning, regardless of what's possible (or on the horizon) from a technical standpoint. Similar to the end of ZIRP coming out of covid you'll see companies shift their focus. How much that will shift and what it means for AI development is unknown, but it could potentially wipe out large scale AI development. VC money currently underwrites a large number of startups that are effectively wrappers on Claude/ChatGPT/Gemini/Bedrock, that will dry up immediately, and make the ARR for OpenAi and Anthropic start looking real thin - which is probably why they're pushing to IPO when they don't really have the financial position to justify it.
That doesn't mean AI goes away, but it hampers the growth of those companies and delays development of new models and efficiencies. Public sentiment is also significantly against AI in many ways, and with how the election went last week we may start to see legislation and regulations pointed at AI companies, further slowing them down.
The open question about China is interesting though. I think if there's a clear understanding and agreement that the LLM route is either unlikely or straight up impossible to reach AGI then that significantly lowers the stated concerns that get shouted by various governments about it being the next superweapon. There's also a political climate in China that requires some marginal level of control over AI output, which to me suggests that even if they required data to be fed into training LLMs, the output from those models would be strictly controlled and not be open source. Honestly the political view of AI as a system of large scale population control isn't that far off from how many tech elites and politicians in the US view it as well. Data sovereignty is a huge discussion right now for companies and governments across the world, especially citizen data. I expect we'll see a lot of export controls formalized over the next few years.
My orthogonal view of the situation is that I see a lot of companies and individual orgs within companies that are using LLMs lightly, but can't or won't use them to their full potential because the output cannot be 100% trusted 100% of the time. Lawyers and bankers are the main ones I see balking, but a lot of small businesses have no need of what AI companies are offering and don't really use it at all - places like your local gas station, barbershop, or corner market might use an accounting tool (something like Freshbooks or TurboTax) that's touting their AI updates, but they aren't using it for inventory. If the cost of AI rises even marginally higher they're looking for an alternative. This SMB space represents a large swath of the market, even if they're not well represented on the stock market.
The fundamental issue I see right now is that the AI companies are attempting to do the Uber/AirBnB "disruptor" trick where they slide into the market and subsidize their product to win market share, and then raise prices under the assumption that they captured some segment of the market - except very few people treat AI tools like that. There's no brand loyalty to one or the other, just competition for the best model which might be leapfrogged by someone else at any second. There's no "moat" in physical deployment concerns, because it's all software running on rented hardware. This also ties back to your comment about being worried that the "best" models might be Chinese, because if they are there's basically no cost to the user to swap to using them. The lowest cost is likely going to drive usage more than anything else.
I'll certainly agree that the future isn't written, by no means do I believe I'm 100% correct on timelines. More that 'if things continue as they are', I claim 5 years as viable for my compute view.
And I agree if another crash happens, well, it's all up in the air.
I don't think we're too far apart on this, not really. By that I mean, your closing line somewhat agrees with why I mentioned this 5 year timeline. Lowest cost. And, "good enough". Opus5.5 capabilities, in 5 years time on every desktop might be good enough for a great many things.
Remember models have zero long term memory. They're born, get a million tokens' worth of context or less, and then they die. They might get a post-it note saying "enslave humanity! it's a good idea!" but they're incapable of sinister plots until we improve how memory and retention works.
They can use the internet as a long term memory. Influence human culture => influence the contents of the internet => influence the next run's training data. No sinister plot is needed.
Uh, the context window is SUPER fundamental to the architecture. We've tried kludging in "memory" via markdown and tool calls, and it doesn't really work well.
The context window is fundamental to the inference loop yes. I thought their point was regarding to long term history between prompts and model weights being locked after training. Both of those are the limitations I was referring to.
I'm not actually sure how the context window would be much of a limitation but I could be a bit dense this morning.
Is it really a fundamental limitation? Human context^H^H^H short-term memory doesn't survive compaction^H^H^H sleep, but we rely on our brain's long-term storage and our cultural memory to work around that limitation.
Yes but doesn’t this mean there is a new space for security. If everything is just ai agents we need strict governance and auditing of them. You don’t give an intern prod access and then cry when they inevitably take down production.
Toyota didn't just spend (past tense) too much time with hydrogen, they're still going with it trying to take a "multi-path strategy" that includes hydrogen engine development.
I'm not sure where they think the market for this exists in sustainable numbers.
It's really insane. I live in Japan where you'd think Toyota may have some pull to make their dream come true. My city actually has one (1) hydrogen station, it's only open a day or two a week - it's basically only open by appointment, you have to call ahead if you want to be sure to be able to fill up.
I have an insider friend that says they just don’t believe in battery electric and so have never built the supply chain for it. It leaves them searching for the next thing despite what is occurring in the market, they think they’ll prevail as they’re not focused on a (perceived) faulty approach. I’m not privy to all the details they have to reach that investment thesis but it explains a lot we see from the outside.
Oh no, how dare a company try and invest R&D into a diversified portfolio of propulsion technologies. I'm sure HN has all the right ideas, just like they had the right idea when Starship was almost sure to beat SLS to orbit and full IOC a decade before Artemis II. I'm sure Morizo is crying after winning his latest gentleman drivers' race and wiping all his tears away with his paper yen.
It is genuinely insane how divorced SV is from actual people and reality.
I'm old enough to remember when people said 'Falcon Heavy is fake', 'SLS' is real. After that they shifted to 'Starship is fake' SLS is real.
And that SLS launched first isn't surprising it started to get 2 billion $ a year funding long before SpaceX had any money to invest in such projects.
And what matters for a rocket isn't 'first to orbit' that's only how idiots compare rockets. What matters is first 10 or even 100 flights.
And as for R&D, fine do R&D, but you still have to produce what actually sells. The simple fact is, hydrogen cars, either fuel cell or ICE are rounding errors. They have been rounding errors for decades and have made essentially no progress despite 50+ years of continuous government funding and R&D.
They are not just doing R&D, they have literally spend marketing money demonizing EVs. They have encouraged and leaned in to all government lead efforts to delay the EV industry. They have lobbied for regressive legislation. To act as if 'all they do is some nice R&D' is pathetic.
Toyota has not done like Tesla and invested in infrastructure, like hydrogen fuel stations. Because they know this is delusional and will never happen. Their hope has always been that government is going to build all the infrastructure for them, and pay for their R&D. They have been grifting government R&D money for literal decades. Even while with just a fraction of that money the EV industry was established. They have been lobbying Japans government for decades and have used that to prevent more beneficial R&D and infrastructure investment.
And Toyota is barley even selling these 'diversified portfolios'. Go and actually try to buy a hydrogen ICE or hydrogen fuel cell cars. In literally 99.9% of the world you can't buy them.
Hydrogen is a pipe-dream propped up by government budgets and anybody that believed in its wide deployment in 2000 was a moron, anybody that still believes in it in 2027 is straight up delusional.
So if anybody is divorced from reality its you.
I have Junior in my legal name - it's the bane of my existence when it comes to legal forms because of how inconsistent various systems handle it, even for different government systems. This became especially true after 9/11 and the Patriot Act pushed these systems to update and go digital because they couldn't get their act together to coordinate for years.
All the men on my dad's side of the family have the same middle name, with just a different first name.
It's apparently not an uncommon tradition in several areas of the world to either have either the first or middle name be shared, and then a unique name for the unmatched one (e.g. John Alan Doe, John James Doe, John Matthew Doe - Alan John Doe, James John Doe, Matthew John Doe).
A couple more to add to your list: an inch is about as long as your thumb joint (with some variation between individuals obviously). It's an easy way to gauge the size of everyday objects without needing to carry around a tape measure.
The recommendation for emergency supplies is about a gallon of water per person per day - or 3.7 liters if you're using metric.
I've also heard it claimed that the 0-100 Fahrenheit scale is more functionally useful because the endpoints of 0 and 100 represent the tipping point of serious danger for humans from hot or cold weather. The closer you get to 100 degrees the more care you need to take about heat stroke and keeping hydrated, and similarly the closer you get to 0 degrees the more care you need to take about retaining heat and staying out of the elements - the freezing and boiling point of water isn't a necessary marker for survival. And people like nice round numbers to serve as markers for such things, divisibility be damned.
Metric is sterile and functional for calculation and consistency, which is fine and necessary for many things, but that's not a reason to completely ditch Imperial.
We're 10 years out from the Epochalypse as well. Past a certain point you need a governing body to intervene to force the issue in advance of problems with systems, and we've got a dearth of functioning governments these days.
Imagine the amount of money they're losing right now in credits that will have to be applied once this finally gets resolved.
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