Kent has gotten this same feedback across practically every single platform that has discussed his issues. He is unable to take critique and will instead just continue to argue and be combative, therefore proving yet again why he is in this situation in the first place
This whole article is built off using DeepSeek R1, which is a huge premise that I don't think is correct. DeepSeek is much more efficient and I don't think it's a valid way to estimate what OpenAI and Anthropic's costs are.
That' what the buzz focused on, strange as we don't actually know what it cost them. While inference optimization is a fact and is even more impactful since training costs benefit from economics of scale.
I don't think that's strange at all, it's a much more palatable narrative for the mass who doesn't know what inference and training is and who think having conversations=training
Because to make GPT-5 or Claude better than previous models, you need to do more reasoning which burns a lot more tokens. So, your per-token costs may drop, but you may also need a lot more tokens.
GPT-5 can be configured extensively. Is there any point at which any configuration of GPT-5 that offers ~DeepSeek level performance is more expensive than DeepSeek per token?
The "efficiency" meantioned in blog post you have linked is the price difference between Deepseek and o1, it doesn't mean that GPT-5 or other SOTA models are less efficient.
Uhhh, I'm pretty sure DeepSeek shook the industry because of a 14x reduction in training cost, not inference cost.
We also don't know the per-token cost for OpenAI and Anthropic models, but I would be highly surprised if it was significantly more expensive than open models anyone can use and run themselves. It's not like they're also not investing in inference research.
That makes the calculation nonsensical, because if you go there... you'd also have to include all energy used in producing the content the other model providers used. So now suddenly everyones devices on which they wrote comments on social media, pretty much all servers to have ever served a request to open AI/Google/anthropics bots etc pp
Seriously, that claim was always completely disingenuous
I don't think it's that nonsensical to realize that in order to have AI, you need generations of artists, journalists, scientists, and librarians to produce materials to learn from.
And when you're using an actual AI model to "train" (copy), it's not even a shred of nonsense to realize the prior model is a core component of the training.
Isn't training cost a function of inference cost? From what I gathered, they reduced both.
I remember seeing lots of videos at the time explaining the details, but basically it came down to the kind of hardware-aware programming that used to be very common. (Although they took it to the next level by using undocumented behavior to their advantage.)
All reports by companies are alleged until verified by other, more trustworthy sources. I don't think it's especially notable that it's alleged because it's DeepSeek vs. the alleged numbers from other companies.
What are we meant to take away from the 8000 word Zitron post?
In any case, here is what Anthropic CEO Dario Amodei said about DeepSeek:
"DeepSeek produced a model close to the performance of US models 7-10 months older, for a good deal less cost (but not anywhere near the ratios people have suggested)"
"DeepSeek-V3 is not a unique breakthrough or something that fundamentally changes the economics of LLM’s; it’s an expected point on an ongoing cost reduction curve. What’s different this time is that the company that was first to demonstrate the expected cost reductions was Chinese."
We certainly don't have to take his word for it, but the claim is that DeepSeek's models are not much more efficient to train or inference than closed models of comparable quality. Furthermore, both Amodei and Sam Altman have recently claimed that inference is profitable:
Amodei: "If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million, and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume, in this cartoonish cartoon example, that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model, in this example, is actually profitable.
What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's much more expensive and requires much more upfront R&D investment. And so the way that it's going to shake out is this will keep going up until the numbers go very large and the models can't get larger, and then it'll be a large, very profitable business, or, at some point, the models will stop getting better, right? The march to AGI will be halted for some reason, and then perhaps it'll be some overhang. So, there'll be a one-time, 'Oh man, we spent a lot of money and we didn't get anything for it.' And then the business returns to whatever scale it was at."
In terms of sources, I would trust Zitron a lot more than Altman or Amodei. To be charitable, those CEOs are known for their hyperbole and for saying whatever is convenient in the moment, but they certainly aren't that careful about being precise or leaving out inconvenient details. Which is what a CEO should do, more or less, but, I wouldn't trust their word on most things.
I agree we should not take CEOs at their word, we have to think about whether what they're saying is more likely to be true than false given other things we know. But to trust Zitron on anything is ridiculous. He is not a source at all: he knows very little, does zero new reporting, and frequently contradicts himself in his frenzy to believe the bubble is about to pop any time now. A simple example: claiming both that "AI is very little of big tech revenue" and "Big tech has no other way to show growth other than AI hype". Both are very nearly direct quotes.
It is not about the present and future value of AI at all. It is about the present and future value of things other than AI. Here is the full quote:
"There is nothing else after generative AI. There are no other hypergrowth markets left in tech. SaaS companies are out of things to upsell. Google, Microsoft, Amazon and Meta do not have any other ways to continue showing growth, and when the market works that out, there will be hell to pay, hell that will reverberate through the valuations of, at the very least, every public software company, and many of the hardware ones too."
I am not doing some kind of sophisticated act of interpretation here. If AI is very little of big tech revenue, and big tech are posting massive record revenue and profits every quarter, then it cannot be the case that "there is nothing left after generative AI" and they “do not have any other ways to continue showing growth” — what is left is whatever is driving all that revenue and profit growth right now!
Bitnami has a number of docker images that are returned by search results (https://hub.docker.com/r/bitnami/redis-sentinel was one that I came across a while ago), and even before this I was concerned about how their images keep getting returned by search results.
I thought I was paranoid, not wanting to have containers that rely on an organization that I didn't know much about (I didn't know that Bitnami was part of Broadcom/VMW), but this just proves my worries were well founded.
It seems lately a lot of comparisons are with Nazi Germany. And there are many questions, they repeat, if people can do any other comparisons.
Can those asking stop for a moment and think, why we have such a situation?
One of the reasons would be that, indeed, we're in a point in time when parallels with Nazi Germany can well be argued for. Political and historical processes are relatively slow, so for us, humans, ten years could seem a lot - we're saying "it's all the time, already for ten years!" - while historically such a period isn't considered long in many cases. In America we don't have the situation of post-WWI Germany, with the rest of Europe mostly ignoring Hitler's preparations, so the struggle in USA looks like a significantly slowed down movie. Step from one side, counterstep from another. Another step, success or retreat again. Counterstep - success or not. There is a trend, but it's slow and, naturally, exploratory - the situation doesn't repeat anywhere near exactly.
So do we still have grounds to compare the situation with Nazi Germany?
What to do? Is it reasonable to label the side "crying wolf" and dismiss them as detached from reality - or does it make sense to take care with serious accusations, figure out what's going on lately - particularly in America - and do the first principles analysis, if the comparison with Nazi Germany is justified? And how much? Wouldn't sometimes better to be cautious and wrong instead of careless and unprepared?
Which history people learn anywhere, not only in USA? The history teach that we don't learn on the history lessons, but - do we study the Nazi Germany enough to not want any kind of it coming back?
> Now, CBA has apologized to the fired workers. A spokesperson told Bloomberg that they can choose to come back to their prior roles, seek another position, or leave the firm with an exit payment.
So no real consequences to the Bank for these underhanded tactics, since this just returns everything back to status quo before the layoffs, perhaps with reduced overall headcount as some workers choose not to return and take the exit payment instead, but surely the numbers still worked well enough that they will do it again but be more crafty about it so they don't lose the appeal.
True, but the union protected its workers from those at the bank. That is the value in the union. In jurisdictions without a union or parity labor policy, these workers would have no recourse for this fraud and the lies.
Absolutely! The union did great. My comment is more about, what is stopping the Bank from doing this again? Because there doesn't really seem to be a downside to attempting it. When they lose, they just have to give everyone their job back, but probably end up ahead due to attrition
The best the union can manage here probably is to be less conciliatory the next time contract negotiations come up.
Oh you want us to take it easy on you with the raise %? Remember that time you fired all of us for no good reason? Yeah our bank accounts still have a hole we're gonna fill up now. Nice try.
So, unless you have a service that requires a fixed number of running instances that is not the same count as the number of servers, I would argue that maybe you don't need Kubernetes.
For example, I built up a Django web application and a set of Celery workers, and just have the same pod running on 8 servers, and I just use an Ansible playbook that creates the podman pod and runs the containers in the pod.
