louiereederson

Also Nietzsche: "“What do the people actually take knowledge to be? What do they want when they want ‘knowledge’? Nothing more than this: something unfamiliar is to be traced back to something familiar.”"

If you’re constantly moving sugar is a necessity for maintaining glycogen. There’s nothing inherently wrong with any food type, it’s all about context.

While they pay a subsidy: > RWE said that it will now reinvest the sum

I'd say Bob Muglia is pretty credible

Over the period of the industrial revolution humanity gained the ability to manipulate the material world in profound ways (convert potential energy into mechanical work, etc.). My view is information technology allows humans to continue to do that, but more efficiently. I think you’d need a breakthrough in manipulating subatomic particles and quantum mechanics to have the same level of relative impact as the industrial revolution.

> Part of the problem was regulators’ decision in late May to approve 16 single-stock leveraged ETFs tracking Samsung and SK Hynix, which have been blamed for amplifying moves in indices and individual stocks.

Baffling that this was approved, though less so if you don't assume the regulator has the general market participant's interests in mind

Commoditization of the model layer is to Azure's benefit. It would help to avoid the model layer becoming a platform that commoditizes cloud into a pure bare metal business. It also redistributes 'consumer' surplus back to the end user via a lower effective price, which would conceivably increase demand for IaaS and/or PaaS which are complements.

Well this at least raises the very interesting question of liability for an illegal action, irrespective of whether the action was undertaken by something considered sentient or a person.

The market is more unpredictable than it’s been in a long, long time so I hesitate to make a firm prediction but to me the odds that SpaceX will be a successful IPO over a 3-6 month window are significantly lower now. S&P inclusion basically requires funds to hold a position by default, and per their own estimates $20tn of assets are indexed/benchmarked to the S&P.

Anthropic's annualized run rate is >$40b according to outside reporting. AWS hit that by Q4 2019. There were still debates on public cloud vs on prem at that time, but by late 2019 public cloud had facilitated the creation or adoption of entire categories of software within SaaS and PaaS, not to mention consumer internet businesses like Uber and Airbnb. The net impact of AI coding tools is far more ambiguous in comparison.

The profitability comparison is fraught but worth noting that by then AWS was already extremely profitable.

I wonder if/when the US limits market entry of Deepseek and other Chinese model vendors like they have done with Huawei

I'm wondering if companies are 'diverting' engineering resources from core products to AI products with the view that the former are legacy. Kind of two sides of the same coin though.

Per sanguinem ad astra

Late last year I tried asking ChatGPT to summarize a collection of 10 researchers' views/findings on a topic and provide representative quotes. It initially looked plausible but when I checked the links, the quotes were from clearly AI generated summaries of actual interviews. The paraphrasing was also plausible but subtly and profoundly incorrect.

I haven't tested this again on the latest models though, so not sure if there's been an improvement.

It’s true, this distracts from the real atrocities

The extension of Full Self Destruct mode

I think as someone pointed out earlier, this is more likely about margin preservation as their gross margins are deteriorating really quickly.

Yikes, so incremental margins are in the 50s. I think this says it all.

Tesla FSD didn't crash your car, you did

This article is about Uber, not Meta

This article seems to fundamentally misunderstand what 'enterprise IT' is all about (enterprise IT being different from IT for a tech-native).

IT is a highly dynamic system, and enterprises optimize for a minimal set of capabilities at the maximum level of abstraction under high levels of uncertainty and different inherited states.

This results in decisions that may not appear technically optimal but which are still an optimal outcome under the extreme uncertainty that an 'enterprise' operates in vis a vis technology paradigms.

Add to this that there is no one technology operating model. everyone has a different starting point, different inherited technical debt. They are optimizing to their own starting point, not a clean slate.

This is what people don't get about what Microsoft actually does - it abstracts both at the technical level and the operational (contracting) level. This is valuable for an organization whose core competency is not technology, even if it does not lead to the most optimal outcomes from a pure technology perspective.

It is possible to question the sustainability of the AI buildout and not have a dogmatic position on AI development.

There are still major unanswered questions here. For instance, all of the incremental data capacity build out is going to businesses that have totally unknown LT unit economics and that today are burning obscene amounts of cash.

More like he wants to ban accelerator chip sales to China, which may be about “national security” or self preservation against a different model for AI development which also happens to be an existential threat to Anthropic. Maybe those alternatives are actually one and the same to him.

Are they war or defense products when they are used against your own citizens?

Chip costs strongly impact the economics of model serving.

It is entirely plausible to me that Opus 4.7 is designed to consume more tokens in order to artificially reduce the API cost/token, thereby obscuring the true operating cost of the model.

I agree though, I chose poor phrasing originally. Better to say that GB200 vs Tranium could contribute to the efficiency differential.

For a 56.7 score on the Artificial Intelligence Index, GPT 5.5 used 22m output tokens. For a score of 57, Opus 4.7 used 111m output tokens.

The efficiency gap is enormous. Maybe it's the difference between GB200 NVL72 and an Amazon Tranium chip?

Surveillance for all

The people in this image remind me of early this person does not exist, in the best way

The image of the messy desktop with the ASCII art is so impressive - the text renders, the date is consistent, it actually generated ASCII art in "ChatGPT", etc. I was skeptical that it was cherry-picked but was able to generate something very similar and then edit particular parts on the desktop (i.e. fixing content in the browser window and making the ASCII dog "more dog like"). It's honestly astounding, to me at least.