Somehow the blog post seems naive. Yes GLM 5.2 is good and cheaper per token, but margins are a result of supply and demand. Now demand for quality and quantity of tokens is increasing at least quadratic or cubic (more users * more tasks * more tokens per task). On the other side you have real infrastructure constraints on the supply side.
Openai and Anthropic have large commitments and contracts that enable them to get access at a scale of compute that is not obviously going to be available for open source model hosts.
And you see it, glm 5.2 inference is less stable and higher variance than any of the bigs labs.
Why is SpaceX not hosting glm 5.2? because they make more money with renting out to Anthropic and Google.
> So he throws billions at a few top AI researchers, but they produce nothing of value.
so he spends 1% of yearly revenue on AI talent to catch up? we can't judge if they have produced nothing of value, no? They don't owe the world to open source their work?
Meta has plenty of failings, but taking risks and investing optimistically is not on my list. I guess the sentiment here on HN is probably biased by the addictive nature of its products.
This is called mechanistic interpretability. There is lots of fascinating insights already since you can do basically everything down to the neuron or weight level thousands of times. The human brain is many orders of magnitude harder to make sense of.
We outsourced it for 2.5k (extra) and it was still painful, took almost 2 months and worst of all wasted so much time and focus.
The worst was sitting at the notary, and getting read out loud by her what we were about to sign (also paying for that).
If you think about starting a company, spend some time to think through what it would mean for you to be a Delaware C Corp or an Estoinian one. It will increase your chances of success as you can focus on what matters.
Like sqlite, duckdb is underappreciated as a production database. You can totally run it on servers or even "serverless" and do some heavy data transformations or with the right server size work with large scale datasets (up to a TB compressed seems fine).
I heard the same argument from my doctor when I wanted a blood scan.
But what's the intention? If you do a scan and then try to find everything that is wrong about you, you're 100% right, there will be false positives and unnecessary panic/medication etc.
However if you just collect data for months and years and WHEN you get a symptom you have a lot more data then we should be able to give better diagnosis faster. If we do that for long enough as humanity and there is data sharing the accuracy of the whole thing will increase a lot.
I do think it's more subtle. AI can replace very few jobs end to end with the same quality, maybe none. But AI can be put to work on high ROI problems. Now when the new marginal job is not obviously as high ROI as putting another 100k of tokens to work, no human gets hired.
Next, comes natural attrition in a company where a certain percentage will leave every year. Will they get replaced with a human or their budget goes into tokens?
Only when these 2 angles are exhausted, a typical company will start thinking about layoffs.
Now, some companies are already stressed: customer buy AI products instead of theirs, AI makes it easier to build what they offer, customers believe they can vibe code things. These companies will layoff first, because of AI. Not because AI will do the persons job but because the money gets spend differently.
Naive question but if it works best wouldn't companies that have a four day work week outperform theirs peers and because of that grow faster, and become more common?
I see the opposite in most startups that have a 6 day work week to get ahead of the "slowly moving" 5 day work week competition.
Techno optimist here who expects the following to make a big contribution to reducing human made future climate change: better batteries+solar/wind, nuclear fusion, self driving cars (we'll need to manufacture less cars for the same amount of miles humanity drives), AI helping with better resource allocation in general (hopefully).
The answer can't be, let's just consume 10x less. We have to engineer our way out of it.