GamingChairModel

This reads like a cross between 2023-era ChatGPT and a mediocre high school student. Who talks like this?

Yeah, it's counterintuitive because it's a lot more work for a human to draw a picture (much less a photorealistic picture) than to write a few words, but human language grammar actually has a lot of strict rules that makes that stream of letters work as "valid" output, much less "decent" output that kinda matches the prompt/description. Transpose a pair of letters or even substitute a single letter (or token) and you've got an output that just doesn't work, in a way that generated images don't have to worry about.

Yeah, the smarter way to use LLM-based agents is carefully defined tasks. Mozilla describes their vulnerability assessment processes in this blog post.

Mozilla describes the process they've used: building a harness that instructs a model to find a specific category of vulnerability on a specific interface, and then write up its findings. It's a narrow enough context that the model gets specific instructions, and a simple definition of success, and it sets up many such tasks that can be fed into the existing process for verifying and triaging bugs. Note that the output for this LLM pipeline basically feeds into the same interface for accepting bug reports from the public, or from their human contributors within the project.

There's a couple of takeaways here, too:

• This pipeline is model agnostic. Mozilla set it up before Mythos was released, and its description of other models (Opus 4.7, Codex) confirms that Mythos is better but not a true game changer. The ability to swap out other models provides some assurance that the work done to develop the pipeline will be useful when cheaper or better models come along, or when a model becomes unavailable (like when a provider decides a particular model is too expensive to run, or a provider goes under).
• The increase in automated output (and presumably automation-assisted contributions from the public) has given the humans more work to do. Automation in this context actually increases the demand for human labor.
• Other projects will need to develop their own custom pipelines, specific to their project, to get good results from LLM based agents.

There are ways to use these tools, but none of it really seems like a truly revolutionary/disruptive change to how large projects are managed.

I think AI will be profitable for the next generation of AI business models that emerge from the abandonment of the current business model of developing the frontier. But the prerequisite is that the companies give up on developing the frontier and decide that the models they have are good enough, then get hardware optimized for inference on those models, stagnating into long term commodity infrastructure, like providing phone service or electricity for profit.

So yeah, I think many of these technologies are here to stay, but the growth will stagnate this year as data center construction swallows up companies that overextended.

It's a Nielsen box that can detect your streaming app activity, too.

In the current environment? Apple shielded itself from price hikes by component suppliers by locking up capacity early. There's a reason why their CEO came up through the supply chain rather than software or design.

The memory Apple is putting in its devices today are largely priced at prices negotiated years ago. It got deals on CPUs and GPUs of their own design, fabricated by TSMC, packaged with Samsung-fabricated memory in System-in-a-Package form, at volumes that make them nearly impossible to say no to, under contracts that are probably bulletproof even as TSMC and Samsung have others clamoring for their capacity at higher prices.

The A18 Pro in the MacBook Neos is made on TSMC's N3E node, which started production in 2023 and was probably under contract by 2022. The AI boom largely started happening after, and the memory/storage chip crunch didn't seem like it would be a problem until 2024 or so.

In an environment like this where there are capacity shortages and companies bidding up the price to absurd levels, companies like Apple are exactly who you'd expect not to be thrown around by price hikes.

Ah, Vista. My "ready for Vista" laptop finally convinced me to try out this Ubuntu Linux people were talking about, and although I dual booted for a few years, by 2009 I never had Windows on one of my own devices again.

Yeah that was the real problem with hot swappable discs. The kids who actually physically performed that operation were often too impatient and careless to put the disc somewhere safe from scratching, because they wanted to immediately get back to playing.

Back in 2002? I don't think they separated generation and delivery for most utilities, at least in the US. In 1996, federal regulators made it mandatory for utilities with delivery infrastructure to accept generators' electricity on fair/nondiscriminatory terms, and gave them some time to implement policies. Then, the actual generators started negotiating deals, but the early days were a bit chaotic, with issues in California with rolling blackouts, then the Enron bankruptcy, and then generators actually entering long term contracts with some price stability in the early 2000's.

For a typical residential customer who didn't go out of their way to look for side deals with generators, they wouldn't have needed to see their bills be segmented out into generation and delivery, since most of the utilities still already had long term contracts (or owned their own generation facilities) still in effect from before the regulatory reform.

Personally, I didn't see those numbers separated out on my bill until around 2009. And I remember my electric bill in 2000-2005 being roughly 10 cents per kwh, flat rate.

And, as I understand it, Anthropic hasn't committed as much spending to building out new data centers, and has setup their operations to be GPU agnostic, so they can keep flexibility between NVIDIA GPUs, Google TPUs, and Amazon Trainium, and play the data center pricing game. Anthropic is better positioned to survive an AI winter (and I believe it's coming soon).

The economics of it don't add up and the growth rate of the curve of improvement over time has already significativelly fallen which looking at the historical curves for other technologies is a very strong indication that it's approaching the limits of how far it will go even though it's nowhere close to the hype.

Yeah, I'm convinced that they've maintained the illusion of continued exponential improvement from 2024-2026 by sneaking in exponential increase in resources (hardware complexity, power consumption), to prop things up past what should have been a plateau.

AI has an interesting economic trait in that it's very, very expensive to deploy, and made very fast progress from 2022 to 2024. That caused investors with money to believe that:

• Pushing the frontier was going to cost a lot of money. More than any other purported revolutionary tech.
• Extrapolation of past improvement meant that whoever was on the cutting edge may end up with a product with a huge paying market.
• So whoever wins this race would be rich, and the investment would have been worth it for them.

But since 2024, we've seen that the cutting edge got even more expensive much faster than expected, and much of the improvements in performance now come from inference rather than training, which represents a high ongoing cost.

Now, if we extrapolate from that trend line, we'll see that the market will be much smaller for AI services at the cost it takes to provide that service, and the question then becomes whether the industry can make its operations cheaper, fast enough to profitably provide a service people will pay for.

I have my doubts they'll succeed, and we might just be looking at the industry like supersonic flight: conceptually interesting, technically feasible, but just a commercial dead end because it's too expensive.