I made a small pantry application to track what foods I have in storage. It can spit out a json blob of that information which I feed into an LLM to make meal suggestions optimizing the ingredients I already have.
No, that's a silly test. If I want to bring in a world renown battery expert from China, it's ridiculous to also add on "and you must speak English well". English fluency has absolutely nothing to do with expertise.
What we actually need is a higher minimum salary for H1B employees. Right now it's something like 50k per year, which is insanely low for a "hard to find expert" it should be more like $300k per year. H1B employees should be some of the best paid employees in a company. Raise that minimum salary and you'll overnight fix almost all complaints with the H1B program. Except for from the business owners who are abusing the system to get cheap labor.
> were/are trying to stop fraudsters, script kiddies, nasty people, and governments from trying to exploit weaknesses and take unauthorised control of devices and services.
While I don't doubt that's a motivation, the problem I have is it's really a question of likelihood. I feel that in terms of security focus it's very common for people to put on blinders and ignore the likelihood of an exploit in favor of "Ooooh look at this thing that COULD be exploited!"
It's fundamentally the problem I have with how CVEs are reported and handled in general.
In terms of secure boot stopping problems. Yes, it does stop someone from rooting a device which is great. However, someone that has access to root a device almost certainly also has the ability to just install a virus in the OS startup scripts. Or to modify a user executable. Or to modify the user's PATH environment variable to inject a malicious app in front of a commonly used one.
That's what I wish security focused people would weigh more heavily when they evaluate these sorts of threats. "What other damage could a malicious individual do if they had the same permissions to pull off this exploit."
> It's fundamentally the problem I have with how CVEs are reported and handled in general.
Yes, its more like a popularity contest.
But secure boot stopped(or stops) a whole bunch of driver/rootkit exploits, which was a big thing in the late 2000s. It means that a random driver that is inserted by some script kiddie raises a whole bunch of warnings, which it wouldnt have done before.
Wow, I think I have almost the exact opposite opinion here.
Java is an ok language, but what really makes it shine is the JVM. It's one of the fastest VMs out there and is one of the most customizable ones as well. For example, pretty much all other languages with a GC have just a GC and that's it. Java allows you to pick and choose your GC based on the workload.
It is one of the least limiting VMs out there because any knob you might want to tune, can be tuned. It's a huge value add.
I think the only part of the JVM that's not great is the fact that objects are bulky and the lack of value classes. Which ultimately means every struct like object you want can have a pretty hefty price in terms of memory. But otherwise, it's best in class basically for everything.
Started in the second half of Biden's presidency. The republicans argued that the IRS was already overfunded in the first half and refused to allocate additional budget for them.
This isn't just a Trump thing, it's a Republican party thing.
This sort of thing is a strong argument (IMO) that these stadiums, arenas, theaters should be owned by the municipality they reside in.
Fine, we can call it a public good which is why they have nice tax incentives. But why stop there? If its truly a public good then why shouldn't the public simply own it? Why isn't the city operating these venues and using the ticket prices to offset tax burdens?
It might be harder to do this with a sports arena as there's a bunch of issues around the monopolies that are the MLB/NBA/etc. But when it comes to a theater style venue, I'd think most artists would be ecstatic to deal with a city rather than ticketmaster. It truly isn't the case that ticketmaster is providing almost anything of value for their venues. And for very large events they have to coordinate with the city anyways.
FWIW I actually prefer we simply tell private businesses that it's their responsibility to provide the environment/setting their employees work in. By that I mean if ticket master wants a monopoly they should have to build the stadiums too. No taxpayer money, incentives, land, or other interest group perks given.
Most cities have lots of venues of various sizes, I like a market place of them rather than a publicly owned monopoly. (Hence why I dont like Canada healthcare either)
I'd argue that the city owning the venue can't be a monopoly as the next city over is a different owner. That might be different if we were talking about the county or state taking ownership. IMO, these things shouldn't be state or federally ran.
But otherwise, yeah I'm happy if their is private competition with the city. I just give more priority to the city owning venues as the people in the city have the ability to vote out the politicians running the venue if they displease them.
Yeah, city law can easily override deed laws. But further, eminent domain allows the city to strip away deed restrictions through a "one weird trick". The city can eminent domain the land from themselves removing the restriction and then sell it privately.
The same way the city can eminent domain your home and put a road through it. The HOA can't stop the city from putting in a new road.
Aren't deed restrictions usually done at the state level? If so, the city can't just magic them away. State law is going to trump city law unless the city's restrictions are tighter.
I think Zitron's problem is he's equating AI to OpenAI and Anthropic. I'd agree with him that both those businesses are in a dangerous position given how fast they've burnt through cash. However, that's not the entirety of the industry and there are a lot smaller labs doing more for a lot less capital.
The business model does appear to be viable for these labs. But that viability comes because they aren't wasting a bunch of R&D money developing worthless products like AI video production.
What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.
If I were to make a prediction, it's that ultimately these cheaper models are going end up eating their lunch. I don't think they'll make back the money they've invested and once that reality hits investors, those two companies are sunk.
That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
It is not just about cheaper models; it is about integration with the economy.
These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
> These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Much like the electric grid, what we are seeing is a convergence on standard APIs. For example, most of these cheaper models are hosted using APIs compatible with OpenAI. It's not a matter of rewiring your electric plug to work with a different socket standard, instead it's just the process of plugging it into a new socket.
> Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
Certainly the Chinese models appear to be some of the best when it comes to competition, but they aren't the only ones. There are European models and other US based models which all run for cheaper.
I see your point, but having worked as a consultant for a few years, I think most companies will opt to stay once things are stable. Once these systems are functional, nobody wants to touch them.
I remember one government project where we wanted to migrate a system from COBOL to a modern stack. The requirement was for the UI to stay exactly the same as the old green terminal; the evaluation criterion was pixel-perfect proximity to the original. We literally had to build terminals using web tech.
These models are not the same as each other. Once they are integrated and working, the incentive to change them is incredibly low. So really, the race is about who can integrate deeper, wider, and faster over the next couple of years—that is what will determine the long-term winners.
This is the exact same playbook we saw with social networks. There is a reason why we have only a handful of them dominating globally, and guess what? It's not because of the tech.
There is no incentive to rewrite working software in COBOL to something else. You don't really change the people cost of maintaining that code all that much and you incur a huge rewrite cost.
AI is different, it's an ongoing cost to the company. If that cost raises aggressively, you can bet companies will race to eliminate it, no matter how integrated it is. Companies can and do do this all the time.
And the models are close, not the same, but close. That's what matters in LLM stuff in general. If a model is capable of doing the same work for less, it will be chosen. Especially since the switch over cost is often on the level of "point the tool at this URL instead of that URL".
I get what you are saying if this were a more sticky concrete tech that is harder to move away from. But that's simply not the case for these LLMs. A big selling point they have is that they are super flexible.
I don't think the transition will be as simple as just flipping a URL. There is an entire legal and technical infrastructure being built around these models and their integration. I think you underestimate an organization's resistance to change once things actually work, as well as the sheer complexity of making that shift.
I also expect pressure will eventually drive the cost of running these models down. Power plants are being built, more capable chips are being produced, and a big chunk of the capital right now is being used to scale the physical infrastructure—the data centers and energy grid. Once that stabilizes, these companies will have positive cash flows. Again, it's highly similar to what we saw with the expansion of social networks, just with more aggressive and widespread adoption.
Ultimately, a handful of companies are going to provide these core capabilities, just like we have a handful of major cloud providers right now. Why do you think this would change? If anything, the trend toward deep vendor lock-in is even stronger now.
The moat is the infrastructure and lock-in. Similar to AWS or anything else. Small data centers can't compete, and similarly people without massive compute won't be able to either (at least not on the enterprise level.) You might get a few edge models, but for huge businesses they will be using OpenAI and Anthropic (and Google/Microsoft/Amazon, etc).
The biggest competitors aren't small models, they are just the traditional players that already have an "in" with enterprises. That I think will start to show its face once this initial round of buildout is complete, which may not be for another 5+ years.
I disagree. Mainly because those small models are exactly what erode away the moat of needing a giant data center. Those smaller models have been proving themselves to not be far of from the SOTA models.
As OpenAI and Anthropic look to raise their prices, businesses will be much more compelled to looking at cheaper models. And if the narrative is "do the same as you did with OpenAI at 1/20th the cost" that's going to sell to a lot of businesses.
It certainly cuts into what exactly these companies can sell in general. For example, if I wanted to integrate AI into a product I'd almost certainly not chose OpenAI or Anthropic. That's because they are simply way too expensive and what they'd give me is a lot less. We've actually ran into just this. We needed a classifier for a lot of records, we picked a free model because, as you can imagine, we didn't need something as good as what OpenAI and Anthopic offered and free works.
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost.
Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost.
That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.
The benchmarks between the two are close and the engineers that have used both (like myself) can attest that the differences aren't so wide as you might believe.
I'd say that yes, ignorance plays a role here because a decent number of engineers are looking strictly at the benchmarks and choosing Opus just for that reason.
But I'd also say that a major factor for Opus use is because Opus is being purchased for the engineers by their employers. They don't get to pick which models they are using.
I find myself rarely reaching for Opus nowadays, it's just too slow. I assume there are tricky use-cases where it's really useful though, just not super relevant for my day to day. I much prefer a faster, "weaker" model.
reply