GamingChairModel

Sure, but the method of rooting that I'm familiar with (and one I suspect is the most popular) is to unlock the bootloader and modify a boot image that you can freely download from Google's website in accordance with their license, and then load and run that boot image onto your device.

Because a cutting edge semiconductor fab takes about $10 billion and 3-5 years to build. So when they made the plans in 2020 based on anticipated demand in 2023-2025, they started the process for manufacturing memory that would be useful for DDR5 specs. Then big orders came in later, during the AI boom of 2023 onward, to claim the cutting edge fabs' 2025 production, to manufacture that very fast memory to be connected directly to logic chips rather than standalone memory packages/DIMMs.

In other industries, production would just pick up to meet the new demand, faster, to claim all that money sloshing around. But when the bottleneck is with something that takes 5-10 years to finance and plan, sudden increases in demand usually result in crazy price volatility.

See also the whiskey market of 2015-2025, where the production of whiskeys that required 12+ years of aging hit a bottleneck, because 2005 producers couldn't have predicted just how much 12-year-old whiskey people would want in 2017.

It just means overcoming software limitations set by the manufacturer.

With you so far.

Rooting an android phone is also jailbreaking

Not if the method to do so consists entirely of steps that the manufacturer allows and documents.

Depending on the manufacturer and model, unlockable bootloaders mean that custom software can be installed without a jail to break out of.

"I outsourced the copywriting to the lowest bidder, who happened to be in Poland"

The U.S. basically made them illegal in workplaces in the 70's, when it was shown that employers were using so-called intelligence tests unrelated to job functions to discriminate on the basis of race. Plus, in the 90's they passed a law banning discrimination on the basis of disability. Now workplace testing needs to be shown to be directly related to job responsibilities, so general purpose tests are pretty much too much of a compliance nightmare to be worth the effort.

Maybe they're still common in some other countries, but they're really rare in the U.S.

Because it obviously was.

The dashes, the short sentences, the bullet points, the overly familiar tone that seems LinkedIn-ish. All of it sounds like AI.

At least they moved onto year-based versioning. That was probably the best part about the 26/Tahoe release.

It's called decoding and encoding.

But the big data centers doing all the video processing for the big video services (including both permanent videos from a library and things like live streaming) are encoding the videos with settings that require less computational power to decode. The idea is to be able to let even old budget smartphones still be able to display the video with very low power requirements on the client device. There's no universe where consumers decoding digital video will be a high-power computational task.

Restaurants have sharp knives in the kitchen, but generally serve food that requires only minimal cutting effort from the table knives set out with the rest of the table settings. Dining will always be easier than cooking, by a margin that makes the difficulty of dining not worth mentioning, so it would be bizarre to criticize a knife as being only good for cooking and eating food, when plenty of dining tableware knives out there would be insufficient for kitchen work.

You've made the mistake of lumping decoding and encoding together based on the algorithmic/mathematical similarity of those tasks, when everyone else is more inclined to discuss the very different end user use cases of those computing needs.

Don't they have their CFO not even reporting directly to be CEO? I would bet that there's a ton of internal dissent about timing and strategy of how to cash out.

Yes. But major differences:

The dot com buildout of physical communications infrastructure involved basically 3 things:

1. Switches/routers at the nodes for sending signals down the right route.
2. Fiber optic cables connecting the nodes.
3. Legal rights of way and easements for the legal right to keep the physical assets in that physical place, and to maintain/replace the stuff as needed.

Category number 1? That stuff went obsolete quickly, and wasn't really reused after the crash.

Category number 2 was better. Turns out, fiber optics can carry signals on a lot more channels than those fibers were originally designed for. And they're designed for useful lives measured in decades. So even if they sat dark from being unused for 5-10 years, eventually they could be used again.

Category 3 is super important. That legal right is basically permanent, and so long as communications equipment needs to physically go from one place to another, having that legal right can be built on and profited on (including the ability to sell or lease those rights).

What's that gonna look like for the AI infrastructure? The servers full of GPUs are the bulk of the cost, and the GPUs are replaced with a new generation every 1-2 years, seem to require all new power and cooling infrastructure every 1-2 generations or so.

Plus the AI buildout looks to be several trillion dollars. Even adjusting for inflation, that's so much more than the tens of billions that each telecom company built out that infrastructure.

And it's hard to see how the servers themselves will be useful for regular businesses, much less consumers. A Blackwell 72-GPU server is $3 million and takes 130 kW to run. A residential electrical line maxes out at about 48kW. The newest Vera Rubin servers are projected to be up to 600kW, with all the power and cooling management that comes with that, plus all the ultra high end networking stuff built into that rack. Even deep pocketed businesses will have trouble finding a use for that server rack worth millions, requiring a ton of supporting infrastructure that not even normal pre-2025 data centers have.

I don't think government funding can actually offset the crash in consumer and business demand being insufficient to cover the cost of the most expensive models on the most expensive GPUs. But if you look through my comment history I've made the comparison to supersonic flight, because I genuinely believe there's a possibility that governments fund the expensive branch of this technology for their own military or surveillance or law enforcement purposes without the benefits necessarily actually spilling out into normal commercial applications.

We've hit the point where training a model (both pre training and post training) isn't the expensive part, and the expensive part is actual inference, which makes it hard to scale the most expensive models to where it's useful for a lot of people. So it might be that the companies and governments that can afford to operate an expensive model might be the only ones to do it. And they'll be able to, without necessarily the public being able to have access to the same tech.

Do you mean the actual packaging of silicon dies and putting them into DIMMs? Yeah, they had to revert back, but that's because a lot of the memory silicon that's only good for DDR4 never shut down, and any silicon memory that is good for DDR5 is also getting claimed up for non-DIMM memory (e.g., memory packaged with logic chips rather than sitting on its own package in a DIMM or even soldered to the board).

Basically, previous generations' silicon fabrication tech is still going, and there are still buyers of that last generation product.