abixb

That's not what I meant at all. I understand no one would want 1:1 text search, and part of the reason why Google won in the 90s was because of PageRank. All I was saying was that basing a 'search engine' on a CoT LLM is very inefficient.

>LLMs designed and deployed properly are infinitely better at this than keyword search engines are, despite being less predictable.

In what way? How are LLMs more efficient and 'infinitely' better at this than what Google had before AI overviews? If anything, I find that search results have gotten worse.

Yes, I'm aware. I meant that deteministic systems should be at the core (PageRank was deterministic), alongside other contextual clues and knowledge graphs -- things Google did before this AI overview crap.

What pisses me off is that you can't turn it off. Like, fine, you may bury and dark pattern the crap out of the disable toggle, but at least give users the choice to opt-out. And no, UDM=14 is not a toggle.

Google is just scared. Google has been scared since November 30, 2022.

Text search is fundamentally a deterministic problem, and squeezing it through a heavy non-deterministic, stochastic engine -- especially through a CoT LLM -- is inefficient from a first-principles algorithm design pov. Also, Google did great search before AI overviews using probabilistic linking and knowledge graphs.

I've been using DuckDuckGo's NoAI version as a fallback, and I've had great results so far; brings me back to the web I remembered growing up -- personal blogs, random forum discussions, and whatnot -- with just a fraction of the page bloat.

I say all this as a HUGE fan of Gemini, Gemma, and Antigravity.

By the way things are going in the industry, I think Gemini 4 Carbon's lead will only persist for the half-life of Francium-223.

The fear will again be if the HybridAgent model is being faithful to pseudo text generated, even if you can read it out partially.

Also, I just finished watching a video on AI 2027 scenario. I don't know if AI companies and researchers are using it as a cautious warning or as a target to hit (or if it's genuinely inevitable).

Yes, thinking in latent space might be the ultimate efficiency hack for CoT LxMs, but going down that path will (near certainly) mean we'll lost control, for one simple reason — humanity will have no way to pause and diagnose.

It'll be like a moody chimp with a button to the dead hand system (Systema Perimeter)

I have a question, and perhaps some of the AI/ML infrastructure experts here could answer: Mistral says, "ML4 was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own datacenters in Europe."

If a 1T params model trained on ~4k NVIDIA GB GPUs could almost match the performance of Kimi's K3 (which is on par with top closed source models of OpenAI/Anthropic) while beating/exceeding other leading SOTA models from top Chinese labs, what are we (in the US) even building these super massive data centers for? Just to churn through more backpropagation reps more quickly?

SpaceXAI's Colossus supercluster in Memphis and Colossus 2 in Memphis/Mississippi (Southaven) are supposed to run into hundreds of thousands to a million GPUs. MSFT's Fairwater GPUs are supposed to have hundreds of thousands as well. So, 3800 GB GPUs are an absolute drop in the bucket. I don't understand the strategy of hyperscalers here, especially with edge inference hardware only getting better from here on (Apple, and all).

Distillation explains some of the advances, but doesn't that mean hyperscalers have a ton of deadweight wrt GPUs sitting on their balance sheets? Will all these GPUs be used for inference once a SOTA model's training checkpoint/batch is done? It's bonkers to me.

Imagine going back in time to late-80s/early-90s, and vividly describing all the tools we have now to folks back then -- coding agents, loops, autonomous multi-hour agentic workflows, automated testing, and PRs, and whatnot. Wonder what their reaction would be.

I sometimes look at my pre-2024 workflows and feel like I'm looking at a caveman's toolchain. Absolutely bonkers as to how quickly we humans adapt to things.

Also, neat bit of trivia: Borland was originally named MIT ("Market In Time"). The OG MIT in Cambridge, MA, sent a trademark letter and they then changed their name to Borland, which was supposedly a client of theirs.

You won't be around to be surprised, not as a human at least. /s

What bothers me about this whole AI tokenomics situation is the lack of transparency. OpenAI and Anthropic have to perhaps be the most opaque companies in existence wrt their offerings. There's like a thousand variables that they can change on the backend at the push of a button which can wildly swing API spends within the same model (partly also due to the non-deterministic nature of LxMs, but still), and there's no objective way to measure them other than vibes.

When the regulations do arrive, I think they should really focus on AI companies and API providers being more transparent wrt how they're billing their customers. Because right now, it's a totally vibes-dependent and a mess.

> At the end of the day, I need to maintain my personal understanding of the BUSINESS use cases + decisions so that I can make judgements that AI would never be able to make.

Exactly. There's no point having a model equivalent to a trillion Einsteins at superhuman speed if you can't verify and judge the outputs of the models for your business, else it might just be as useful as comparing water displacement capacity of Niagara falls to your bathtub.

These tools have to be human centered. AI brained tech bros believe that singularity is a good thing — no it isn't. We don't want a world where normal laws and rules and regulations breakdown -- chaos is the word.