alfalfasprout

...how is this "anti AI content"? This is a real issue.

none of these are moats. And OAI is right on their heels.

Which makes it a very reasonable take. AI as a research and ideation tool is invaluable (provided due diligence is done on sources, etc.)

AI generated writing is (usually) hot garbage.

It's trivial to hit that kind of quota if you're trying to execute on major projects. Especially as you start having dozens or hundreds of subagents investigating, prototyping, and working on different things.

Yeah, and synthetic data is pretty darn good nowadays especially with access to frontier models.

The moat for OAI and anthropic seems to be very quickly shrinking. Chinese labs are now using RSI-like approaches and even without resorting to heavy distillation they're catching up in a couple of months vs. what would have been 6-12 months a year prior.

And as these models get better the pace of training is quickly speeding up too.

This doesn't bode particularly well for anthropic/OAI after they go public.

assuming demand remains elevated and growing, maybe. But spend on AI is pretty stratospheric right now... companies are already starting to clamp down on spend. This makes you really wonder if there will be sufficient demand at current commodity prices for eg; OSS models to justify all these data centers.

there are many things to blame POTUS for. This isn't one of them... most major fabs were already outside the US to begin with and the US chipmaking industry (well, really Intel) was really struggling even years ago.

How is this not a frontier model? It's bleeding edge in its own niche. It's not a frontier LLM; however, applicable to many of the things people use LLMs for.

very HN take. Reality is 3.8 flash is good at what it does (being fast + cheap)... but that market is becoming very saturated and google is not competing at all at the frontier where major spend lies.

Yeah but politicians get to claim this is a win. Let's be honest, that's the real reason the prosecution went for a deal this small.

Probably Finnegan's Wake

btw it's colombia not columbia.

If anything, AI tends to vastly overengineer things (in a bad way) which makes bloat inevitable. But I'm not sure layers of management are to blame. If anything, good management incentivize the right things and focus a team on work that matters. Bad managers only try to empire build and incentivize the wrong things in their engineers.

I've seen orgs that try to flatten too much end up with ICs basically left in the ether without meaningful direction which causes them to 'build to survive'.

Neither of them are "almost there"...

The weights are open, the training data obviously isn't. But a key thing you can do with open models is fine tune them. So while they may not be SOTA at everything, they can become SOTA at your particular business use case.

There's a massive difference between your kids vibe coding something and an engineer using AI to implement something. If you're unable to discern the difference, that's something to reflect on :)

On the flipside, Apple has realized AI models are quickly becoming a commodity. By not blowing hundreds of billions in speculative capex, they get to focus on the bits that likely have higher ROI: the touchpoints with AI and how it's used.

If anything, it's becoming more true. In the last month you already have Kimi K3 reaching competitive frontier performance (in practice, a bit behind Fable/Sol but close enough). Sure, it's a chinese model but the reality is it puts dramatic pricing pressure on the main players. And these models are now getting good enough to actually help accelerate further AI model research which would suggest the gap might close even more in the near future.

Sundar Pichai began the downfall of Google by most accounts. It went from an engineer driven culture to one of infighting, politics, and bureaucracy. It's telling when xooglers nowadays complain of maintenance and improvement of products being a career dead end vs. shipping new things. And there being so much empire building.

Did you even read the post? The idea is to tie conversations to code. This is a more sophisticated version of something like doltdb, not a git replacement.

This is going to kill someone... you can't just make these calls for people. And it's DEFINITELY not a replacement for snowpack tests, etc. at the location you're going to visit.

small downsides like security holes? Those aren't small. Neither is creating a codebase that's an inextensible mess that even LLMs begin to struggle with.

The reality is making good decisions and thinking about approaches take time. AI can absolutely make us faster at it but it's not magic and these speedups come with effort.

This is probably one of the more level headed takes in the comment thread. There's been a concerted marketing push to frame AI maximalism as an inevitability. More or less a "it's going happen anyways so let's go all in".

It's hardly an inevitability though (nothing is... and analogues to the industrial revolution are iffy at best, we haven't ever had an attempted replacement for intelligence itself before).

Society is doing this at an unprecedented cost and it's clear a large portion of the population is uneasy with it. Whether society in the US, Europe, and Asia will continue to allow such investment at the expense of everything else remains to be seen.

A part of the puzzle that rarely gets discussed is something that predated LLMs entirely-- "software engineering" and "programming" have been conflated for a long time now and there's a huge gamut of roles out there.

The practice of writing code, or programming, in recent years has really fallen into two buckets:

The vast majority of folks are given a task, they write code to complete that task, and the task completion then counts towards some objective (eg; a new feature, product or fixing a bug). Perjoratively, they've been known as "ticket takers".

A much smaller group have instead worked in the other direction-- identifying where improvements can be made to a product, piece of infrastructure, or pain point and transformed that into tasks that can then be solved via code.

How much of a role you play in that strategy and formulation has been the real differentiator. Not so much what you know. While these are correlated, they're very different.

At a high level, it's been the difference between "developer" and "engineer" but the reality is the titles have become somewhat meaningless in recent years where many "engineers" are just doing the same CRUD tasks over and over.

The reason this matters is that at some point, you can only abstract so far... the requirements for what to build have to come from somewhere. At the most extreme case, there's only the CEO and a company that's nothing but AI agents. In the least extreme case (today) each line worker could manage 1 or more LLMs/agents.

It's not entirely clear to me or frankly a large portion of those in the industry that we're suddenly on pace for one outcome vs another. But I do think that software isn't particularly unique here other than it was an initial starting point for LLMs to deliver value. All white collar work is at risk including CEOs.

And if that happens it would be outlandish to think a utopia emerges... the opposite is far more likely.

It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.

It's not just that there's a circular deal it's that they're prevalent. And worse, with frontier labs IPOing seeking astronomical valuations that means a lot of the public is now exposed too (even if they don't all get fast-tracked into eg; the SP500).

The problem is the valuations assume astronomical growth... that is likely impossible for all of them to simultaneously achieve. Which means something's got to give.

> the big providers are charging full freight for inference

They're not and it's not clear why you seem to believe that. The immense capex for buildouts, training costs, etc. are not rolled into inference costs. Moreover, companies are already rapidly starting to re-evaluate token spend.

Generally, I agree because what happens is the messaging around AI is doing more, faster. Not using AI to deliver at a higher quality level, etc. But I think it boils down to incentives and discipline. So given the incentives we have today at most workplaces faster AI will just be used to produce more slop.

Absolutely! Yes. This rhetoric of inevitability only benefits these AI companies.